{"cells":[{"metadata":{},"cell_type":"markdown","source":"# VinBigData Chest X-ray Abnormalities Detection competition\n\nAs the [competition overview](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/overview) describes (if you did not read it, read it - including the linked materials they provide!), the goal in this competition is to localize and classify 14 types of chest abnormalities on images (chest radiographs). For that, we are given 15,000 labeled images (with labels provided by 3 separate radiologists that can - **and will** - disagree) as training data. The test data is 3,000 images and these were labelled based on the **consensus** of 5 radiologists. Thus, the training data we are given is not actually labelled in the way the test data were labelled.\n\n## Table of contents\n1. [Labelling process that created the training and test data](#1)\n2. [First part of the data: train.csv](#2)\n3. [Second part of the data: .dicom image files](#3)\n4. [Where do the different findings tend to be?](#4)\n5. [How big do bounding boxes tend to be for different classes? How many are there?](#5)\n6. [What is in the .dicom meta-data?](#6)\n7. [Creating fast to read shelve file](#new7) \n8. [Example images of each class](#8new)\n9. [Some thoughts on cross-validation](#7)\n10. [Some initial thoughts on data augmentation](#8)\n11. [Implementing augmentations with bounding boxes](#9)\n12. [Possible data issues](#11a)\n13. [What are we asked to predict, exactly?](#10)\n\nThe data comes in two parts: 1. a train.csv file and 2. .dicom image files, so one key part of what we'll look at is what these files contain. A lot of the EDA is also looking at data across the files (e.g. showing bounding boxes on the images, looking at what areas findings tend to be in). I will also look at some more modeling related questions such as cross-validation, data augmentation and the expected modeling output.\n\nAnother important thing is how we read the data fast during model training. I have a suggestion using `shelve` (like pickle, but allows parallel reading and access via dictionary keys).\n\nBut, first, let's talk about how the labels were created.\n\n<a id=\"1\"></a>\n# 1. Labelling process that created the training and test data\n\nGiven that the data generating process is often rather important for deciding on cross-validation and other modeling decisions, one should definitely read [the paper](https://storage.googleapis.com/kaggle-media/competitions/VinBigData/VinDr_CXR_data_paper.pdf) that provides details on how the dataset was buildt - in fact, the EDA they do in there is pretty good as a starting point (and it's one of the better examples of explaining the competition data clearly that I've seen on Kaggle). Figure 1 of the paper shows this data flow:\n![screenshot1.jpeg](attachment:screenshot1.jpeg)\n\nNote that the paper describes for the test data that:\n> For the test set, 5 radiologists involved into a two-stage labeling process. During the first stage, each image was independently annotated by 3 radiologists. In the second stage, 2 other radiologists, who have a higher level of experience, reviewed the annotations of the 3 previous annotators and communicated with each other in order to decide the final labels. The disagreements among initial annotators were carefully discussed and resolved by the 2 reviewers. Finally, the consensus of their opinions will serve as reference ground-truth.\n\nThis single final consensus is what we are evaluated against on the test set. In a way, the second stage of the test set labelling is missing / was not done for the training data.\n","attachments":{"screenshot1.jpeg":{"image/jpeg":"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"}}},{"metadata":{},"cell_type":"markdown","source":"## Looking in more detail at the radiologist performance\nThis interesting [discussion](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/discussion/215444) in the forums prompted me to look a bit more into the details of how the 17 different radiologists performed the scoring. The question is - since we have annotations by three different radiologists and no single ground-truth - how do we combine these annotations? Ideas include using all annotations in turn/different ones in different epochs of training and somehow combining them to imitate the consensus process. For the latter part, it would be interesting, if we could account for the skill of the radiologists. Perhaps some radiologist even tend to make particular mistakes (whether those are misinterpretations or software usage issues that keep happening to the same person).\n\nWhen we look at how much class 14 (=\"no finding\"; see below for explanation of classes) gets assigned at an image level (rather than at a bounding box level), it looks to me like some X-rays were a-priori believed to be without a finding (= `class_id` 14) and those were predominantly assigned to certain radiologists, especially `R1` to `R7`. On the other hand, `R8`, `R9` and `R10` mostly did get images with findings. However, we can see that the other radiologists `R11` to `R17` also reviewed quite a few \"no findings\" images. Of course, there seems to have been some mixing up of who reviews together. When they co-reviewed, everyone seems to mostly agree on those \"no findings\" cases. None of this is mentioned in the paper - especially how the prescreening for \"a-priori no findins\" images worked."},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\ntrain = pd.read_csv('../input/vinbigdata-chest-xray-abnormalities-detection/train.csv')\n\nwhatassigned = train[['rad_id', 'class_id', 'image_id']]\\\n    .groupby(['rad_id', 'class_id'])\\\n    .count()\\\n    .reset_index()\\\n    .pivot(index='rad_id', columns='class_id',values='image_id')\\\n    .add_prefix('class')\\\n    .fillna(0)\\\n    .astype(np.int64)\nwhatassigned['Percent with no finding'] = [f'{tmpvar}%' for tmpvar in np.round(100*whatassigned['class14'].values/whatassigned.sum(axis=1).values,2)]\nwhatassigned","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"When we look at how much radiologists agree with each other, we immediately see that some have fantastic agreement with their peers, **but** that's primarily those radiologists that soely (or mostly) reviewed the cases with no findings. So, it's difficult to see how much we should read into this."},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"rad_id_counts = (train.assign(counter=1))[['rad_id', 'image_id', 'class_id', 'counter']]\\\n    .drop_duplicates()\\\n    .pivot(index=['rad_id', 'image_id'], columns='class_id', values='counter')\\\n    .add_prefix('class')\\\n    .fillna(0)\\\n    .astype(np.int8)\\\n    .reset_index()  \n\nrename_dict = {'rad_id': 'rad_id2'}\nrename_dict.update({f'class{class_id}': f'class{class_id}_2' for class_id in range(15)})\nmerge1 = pd.merge( rad_id_counts,\n                   rad_id_counts.rename(columns=rename_dict),\n                  on='image_id', how='outer' )\nmerge1 = merge1[merge1['rad_id'] != merge1['rad_id2']]\n\nrename_dict = {'rad_id': 'rad_id3'}\nrename_dict.update({f'class{class_id}': f'class{class_id}_3' for class_id in range(15)})\nmerge2 = pd.merge( merge1,\n                  rad_id_counts.rename(columns=rename_dict),\n                  on='image_id', how='outer')\nmerge2 = merge2[ (merge2['rad_id'] != merge2['rad_id3']) & (merge2['rad_id2'] != merge2['rad_id3'])]\n\nfor class_id in range(15):\n    merge2[f'Agreed with both colleagues on class {class_id}'] = (merge2[f'class{class_id}']==merge2[f'class{class_id}_2']) & (merge2[f'class{class_id}']==merge2[f'class{class_id}_3'])\n    merge2[f'Agreed with one colleague on class {class_id}'] = (merge2[f'class{class_id}']==merge2[f'class{class_id}_2']) | (merge2[f'class{class_id}']==merge2[f'class{class_id}_3'])\n\nclass_agree_both_cols = [f'Agreed with both colleagues on class {class_id}' for class_id in range(15)]\nmerge2[f'Agreed with both colleagues on all classes'] = merge2[class_agree_both_cols].min(axis=1)\nclass_agree_one_cols = [f'Agreed with one colleague on class {class_id}' for class_id in range(15)]\nmerge2[f'Agreed with at least on colleague on all classes'] = merge2[class_agree_one_cols].min(axis=1)\n\nfor class_id in range(15):\n    merge2[f'When proposing class {class_id} both colleagues agree'] = np.where(merge2[f'class{class_id}']==1,\n                                                                                (merge2[f'class{class_id}']==merge2[f'class{class_id}_2']) & (merge2[f'class{class_id}']==merge2[f'class{class_id}_3']), \n                                                                                pd.NA)\n    merge2[f'When proposing class {class_id} at least one colleague agrees'] = np.where(merge2[f'class{class_id}']==1, \n                                                                                        (merge2[f'class{class_id}']==merge2[f'class{class_id}_2']) | (merge2[f'class{class_id}']==merge2[f'class{class_id}_3']), \n                                                                                        pd.NA)\n\nclass_agree_both_cols = [f'When proposing class {class_id} both colleagues agree' for class_id in range(15)]\nmerge2[f'When proposing classes for record both colleagues agree'] = merge2[class_agree_both_cols].min(axis=1)\nclass_agree_one_cols = [f'When proposing class {class_id} at least one colleague agrees' for class_id in range(15)]\nmerge2[f'When proposing classes for record at least one colleague agrees'] = merge2[class_agree_one_cols].min(axis=1)\n#print(merge2[f'When proposing classes for record both colleagues agree'].mean())\n#print(merge2[f'When proposing classes for record at least one colleague agrees'].mean())\n\n\nfor colname in list(merge2.columns[-66:]):\n    merge2[colname] = 1.0*merge2[colname]\nsummary_cols = ['rad_id'] + list(merge2.columns[-64:])\n\npd.set_option('display.max_rows', 500)\npd.set_option('display.max_columns', 500)\npd.set_option('display.width', 1000)\n\nhowmany = merge2[['rad_id', 'image_id']]\\\n    .groupby('rad_id')\\\n    .count()\\\n    .rename(columns={'image_id':'Images assessed'})\\\n    .reset_index()\\\n    .assign(Metric='Images assessed')\\\n    .pivot(index='Metric', columns='rad_id', values='Images assessed')\\\n    .reset_index()\n\ntmp1 = merge2[summary_cols]\\\n    .melt(id_vars='rad_id', var_name='Metric', value_name='Value')\n # = tmp1['Value'].astype(float)\ntmp1['Value'] = pd.to_numeric(tmp1['Value'], errors='coerce') #.value_counts(dropna=False)\ntmp1 = tmp1\\\n    .groupby(['rad_id', 'Metric'])\\\n    .mean()\\\n    .reset_index()\\\n    .pivot(index='Metric', columns='rad_id', values='Value')\\\n    .reset_index()\n\npd.concat([howmany, tmp1])[ ['Metric'] + [f'R{rad_id}' for rad_id in range(1,18)] ]","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"import os\nimport re\nimport pandas as pd\nfrom fastai.medical.imaging import *\nfrom fastai.vision.all import *\nimport numpy as np\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as ptc\nfrom tqdm import tqdm # for getting a progress bar on loops","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"2\"></a>\n# 2. First part of the data: train.csv\n\nThe `train.csv` file gives the outcomes of the review of each image (unique ID is `image_id`) by 3 radiologists (unique ID `rad_id`). And all classes the radiologist has assigned per image. Each class (`class_id` matching up to `class_name`) for an image is in a separate record. So, in case of a `No finding` or a single finding, there would be just one recod for the combination of `image_id` and `rad_id`, in case of multiple findings there would be multiple records. In addition there is a bounding box given for where the finding is on the image, which is indicated in terms of a rectangle given by a minimum x (`x_min`), maximum x (`x_max`), minimum y (`y_min`) and maximum y (`y_max`) values. When there is no finding, these coordinates are `NaN`."},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train = pd.read_csv('../input/vinbigdata-chest-xray-abnormalities-detection/train.csv')\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"In the data we have the following classes (the supplementary material of the paper describing the competition data explaines the medical definiton of the classes) with the following `class_id` values from 0 to 14:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train[['class_id', 'class_name', 'rad_id']].groupby(['class_id', 'class_name']).count().rename(columns={'rad_id': 'Number of records'})","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"So, what do these categories mean? This is actually explained in the [paper linked on the competition homepage](https://arxiv.org/pdf/2012.15029.pdf):\n0. **Aortic enlargement**: \"An abnormal bulge that occurs in the wall of the major blood vessel.\"\n1. **Atelectasis**: \"Collapse of a part of the lung due to a decrease in the amount of air in the alveoli resulting in volume loss and increased density.\" (see also [Merck Manual](https://www.merckmanuals.com/professional/pulmonary-disorders/bronchiectasis-and-atelectasis/atelectasis?query=Atelectasis))\n2. **Calcification**: \"Deposition of calcium salts in the lung.\" - one article I looked at says that \"\\[...\\] calcifications occur in a damaged lung following an inflammatory process such as infection (tuberculosis, histoplasmosis, Pneumocystis carnii), bleeding or pulmonary infarction\" ([Bendayan et al. 2000](https://dx.doi.org/10.1053/rmed.1999.0716))\n3. **Cardiomegaly**: \"Enlargement of the heart, occurs when the heart of an adult patient is larger than normal and the cardiothoracic ratio is greater than 0.5.\"\n4. **Consolidation**: \"Any pathologic process that fills the alveoli with fluid, pus, blood, cells (including tumor cells) or other substances resulting in lobar, diffuse or multifocal ill-defined opacities.\"\n5. **ILD**: \"Interstitial lung disease (ILD) Involvement of the supporting tissue of the lung parenchyma resulting in fine or coarse reticular opacities or small nodules.\" (see also [Merck Manual](https://www.merckmanuals.com/professional/pulmonary-disorders/interstitial-lung-diseases/overview-of-interstitial-lung-disease?query=interstitial%20lung%20disease))\n6. **Infiltration**: \"An abnormal substance that accumulates gradually within cells or body tissues or any substance or type of cell that occurs within or spreads as through the interstices (interstitium and/or alveoli) of the lung, that is foreign to the lung, or that accumulates in greater than normal quantity within it.\"\n7. **Lung Opacity**: \"Any abnormal focal or generalized opacity or opacities in lung fields (blanket tag including but not limited to consolidation, cavity, fibrosis, nodule, mass, calcification, interstitial thickening, etc.).\" (much more detail in this [Kaggle notebook](https://www.kaggle.com/zahaviguy/what-are-lung-opacities))\n8. **Nodule/Mass**: \"Any space occupying lesion either solitary or multiple.\"\n9. **Other lesion**: \"Other lesions that are not on the list of findings or abnormalities mentioned above.\"\n10. **Pleural effusion**: \"Abnormal accumulations of fluid within the pleural space.\" (see also [Merck Manual](https://www.merckmanuals.com/professional/pulmonary-disorders/mediastinal-and-pleural-disorders/pleural-effusion?query=Pleural%20effusion))\n11. **Pleural thickening**: \"Any form of thickening involving either the parietal or visceral pleura.\" (see also [Radiopaedia](https://radiopaedia.org/articles/pleural-thickening))\n12. **Pneumothorax**: \"The presence of gas (air) in the pleural space.\" (see also the [Merck Manual](https://www.merckmanuals.com/professional/pulmonary-disorders/mediastinal-and-pleural-disorders/pneumothorax?query=Pneumothorax))\n13. **Pulmonary fibrosis**: \"An excess of fibrotic tissue in the lung.\"\n14. **No finding**: Kind of self-explanatory (there were no findings).\n\nI have added example images of each class later on in Section [Example images of each class](#8new)."},{"metadata":{},"cell_type":"markdown","source":"How is the occurence of the different classes correlated? Unsurprisingly, no finding is negatively correlated with all findings, while the various findings are slightly positively correlated, except for a few exceptions with strong correlations such as '0: Aortic enlargement' and '3: Cardiomegaly'."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"tmpdf = train[['class_id', 'image_id', 'rad_id']].groupby(['class_id', 'image_id']).count().reset_index()\ntmpdf['rad_id'] = np.minimum(tmpdf['rad_id'].values, 1)\ncorr  = tmpdf.pivot(index='image_id', columns='class_id', values='rad_id').fillna(0).reset_index(drop=True).corr()\ncorr.style.background_gradient(cmap='coolwarm', vmin=-1.0, vmax=1.0).set_precision(2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"records = len(train)\nunique_images = len(np.unique(train.image_id))\n\nmin_records = np.min( train[['image_id', 'class_id']].groupby('image_id').count().reset_index().class_id )\nmean_records = np.mean( train[['image_id', 'class_id']].groupby('image_id').count().reset_index().class_id )\nmedian_records = np.median( train[['image_id', 'class_id']].groupby('image_id').count().reset_index().class_id )\nmax_records = np.max( train[['image_id', 'class_id']].groupby('image_id').count().reset_index().class_id )\n\nprint(f'There are {records} records and {unique_images} images as per image_id.')\nprint(f'The number of records per image is a mean of {mean_records} (median {median_records}) with a minimum of {min_records} and a maximum of {max_records}')\n\ndedup = train[['image_id', 'rad_id']].drop_duplicates()\n\nmin_records = np.min( dedup[['image_id', 'rad_id']].groupby('image_id').count().reset_index().rad_id )\nmean_records = np.mean( dedup[['image_id', 'rad_id']].groupby('image_id').count().reset_index().rad_id )\nmedian_records = np.median( dedup[['image_id', 'rad_id']].groupby('image_id').count().reset_index().rad_id )\nmax_records = np.max( dedup[['image_id', 'rad_id']].groupby('image_id').count().reset_index().rad_id )\n\nprint(f'The number of radiologists (rad_id) per is exactly 3 (minimum of {min_records} and a maximum of {max_records}).')\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let's look at the records for one image with 3 records in the `train.csv` file:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train[train['image_id']=='000434271f63a053c4128a0ba6352c7f']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Now, let's see some records for the image with 57 records:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train[train['image_id']=='03e6ecfa6f6fb33dfeac6ca4f9b459c9']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"3\"></a>\n# 3. Second part of the data: .dicom image files\n\nI use the [fastai medical imaging tools](https://docs.fast.ai/medical.imaging.html#Path.dcmread) (note there's also a nice [tutorial by Jeremy Howard on dicom gotchas](https://www.kaggle.com/jhoward/some-dicom-gotchas-to-be-aware-of-fastai)), but that uses [pydicom](https://pydicom.github.io/) in the background, which is another library you could use. A recent competition with .dicom files was [OSIC Pulmonary Fibrosis Progression](https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression), but there have been several others, so these might be worth checking out for the notebooks and ideas for image processing that were shared.\n\nSo, what's in a [.dicom](http://) file? Let's take a look. We'll use an image that has some abnormalities:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train[train['image_id']=='9a5094b2563a1ef3ff50dc5c7ff71345']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"TEST_DCM = Path('../input/vinbigdata-chest-xray-abnormalities-detection/train/9a5094b2563a1ef3ff50dc5c7ff71345.dicom')\ndcm = TEST_DCM.dcmread()\ndcm","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"dcm.show();","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Note that there's one single channel in these images (monochrome), so they will not easily work with neural networks pre-trained with multiple color channels. However, fastai has a [helper function](https://docs.fast.ai/medical.imaging.html#Tensor.to_3chan) that returns a version with 3 (or a different number of channels). Even if you do not want to use the library, you can always look at the source code of the function and see what helps you. Also, in the augmentations section later, I show another way of achieving the same thing."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"plt.figure(figsize=(16, 28));\nfig,ax = plt.subplots(1);\n\nax.imshow(dcm.pixels, cmap='gray');\nrect1 = ptc.Rectangle((691,1375),1653-691, 1831-1375,linewidth=1,edgecolor='r',facecolor='none'); # (x, y), width, height\nax.add_patch(rect1);\n\nrect2 = ptc.Rectangle((1789,1729),1875-1789, 1992-1729,linewidth=1,edgecolor='b',facecolor='none'); # (x, y), width, height\nax.add_patch(rect2);\nrect3 = ptc.Rectangle((692, 1375), 1657-692, 1799-1375,linewidth=1,edgecolor='b',facecolor='none'); # (x, y), width, height\nax.add_patch(rect3);\nrect4 = ptc.Rectangle((1052,715),1299-1052, 966-715,linewidth=1,edgecolor='b',facecolor='none'); # (x, y), width, height\nax.add_patch(rect4);\n\nrect5 = ptc.Rectangle((689,1313),1666-689, 1763-1313,linewidth=1,edgecolor='g',facecolor='none'); # (x, y), width, height\nax.add_patch(rect5);\n\nplt.show();","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"As we can see, the 3 radiologists (I used a different color for each radiologist) roughly agreed on one area of interest and they all said there was `Cardiomegaly`, but there were two other areas only highlighted by one of the radiologists. Additionally, the one that highlighted the extra areas, also gave two separate labels to the central area that everyone higlighted.\n\nSo, clearly, we are dealing with label noise here.\n\nAs we can see from  [the paper](https://storage.googleapis.com/kaggle-media/competitions/VinBigData/VinDr_CXR_data_paper.pdf) sponsors wrote, the test set labels are based on the consensus of 5 radiologists. Without a lot of subject matter expertise, it's of course rather hard to guess what the consensus of radiologists would be after each coming up with the slightly disagreeing labels and bounding boxes."},{"metadata":{},"cell_type":"markdown","source":"I have to admit, the plots they made in the paper such as Figure 3\n![Figure 3 of the paper by the sponsors on the competition data](attachment:Screenshot%20from%202020-11-20%2015-26-33.jpeg)\nare really nice and very nicely 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id=\"4\"></a>\n# 4. Where do the different findings tend to be?\n\nAs we can see below, certain findings tend to concentrate in certain area - we'll ignore that images might not all be the same size. E.g. aortic enlargment is where the aorta is, cardiomegaly is around the heart, etc., which may be useful for checking the plausibility of model outputs/knowledge we might try to explicitly provide to networks (that of course assumes that all images show upper bodies from a similar distance)."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"locations = np.zeros((14, int(np.ceil(np.max(train.y_max)/10)*10), int(np.ceil(np.max(train.x_max)/10)*10)))\nfor index, row in tqdm(train.iterrows(), total=train.shape[0]):\n    if row['class_id']<14:\n        locations[row['class_id'], int(row['y_min']):int(row['y_max']+1), int(row['x_min']):int(row['x_max']+1)] += 1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"classes = train[['class_id', 'class_name', 'rad_id']].groupby(['class_id', 'class_name']).count().rename(columns={'rad_id': 'Number of records'}).reset_index()\n\nfor index, row in classes.iterrows():\n    if index==0:\n        label_dict = {row['class_id']: row['class_name']}\n    else:\n        label_dict.update({row['class_id']: row['class_name']})","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"f, axs = plt.subplots(5, 3, sharey=True, sharex=True, figsize=(16,28));\n\nfor class_id in range(14):\n    axs[class_id // 3, class_id - 3*(class_id // 3)].imshow(locations[class_id], cmap='inferno', interpolation='nearest');\n    axs[class_id // 3, class_id - 3*(class_id // 3)].set_title(str(class_id) + ': ' + label_dict[class_id])\n    \nplt.show();","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"5\"></a>\n# 5. How big do bounding boxes tend to be for different classes? How many are there?"},{"metadata":{},"cell_type":"markdown","source":"Let's first look at the total area of each box for a class:"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"cols = ['#e41a1c', '#377eb8','#4daf4a','#984ea3','#ff7f00','#ffff33','#a65628','#f781bf','#999999', '#000000', '#1b9e77', '#d95f02', '#7570b3', '#e7298a']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train['size'] = (train['x_max']-train['x_min'])*(train['y_max']-train['y_min'])\nsizes = train.loc[train['class_id']<14, ['class_id', 'size']].groupby('class_id').mean().reset_index()\n\nplt.figure(figsize=(16, 8));\nplt.bar(sizes['class_id'], sizes['size'], \n        tick_label=[str(i) + ': ' + label_dict[i] for i in range(14)],\n        color=cols);\nplt.xticks(rotation='vertical');","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Now total area for a class in an image (i.e. before we looked how large an average bounding box for a class is, here we look at the summed area of the bounding boxes within a class within an image):"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"sizes2 = train.loc[train['class_id']<14, ['image_id', 'rad_id', 'class_id', 'size']].groupby(['image_id', 'rad_id', 'class_id']).sum('size').reset_index().groupby('class_id').mean('size').reset_index()\n\nplt.figure(figsize=(16, 8));\nplt.bar(sizes2['class_id'], sizes2['size'], tick_label=[str(i) + ': ' + label_dict[i] for i in range(14)],color=cols);\nplt.xticks(rotation='vertical');","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Finally, how many bounding boxes does a radiologist typically draw when he or she has identified a particular class in an image? As we can see certain classes (rather logically) typicall just have one bounding box, like e.g. \"0: Aortic enlargement\" or \"3: Cardiomegaly\", while others like \"8: NoduleMass\" tend to have more."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"numbers = train.loc[train['class_id']<14, ['image_id', 'rad_id', 'class_id', 'size']].groupby(['image_id', 'rad_id', 'class_id']).count().reset_index().groupby('class_id').mean('size').reset_index()\n\nplt.figure(figsize=(16, 8));\nplt.bar(numbers['class_id'], numbers['size'], tick_label=[str(i) + ': ' + label_dict[i] for i in range(14)], color=cols);\nplt.xticks(rotation='vertical');","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let's have a closer look at which categories have more than one bounding box per radiologist. That's an [important question](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/discussion/211035) when you start to consolidate the bounding boxes from different annotators. I.e. should you for certain categories only ever create one bounding box? for aortic enlargement and cardiomegaly (plus, of course, for \"No finding\", you can only ever by definition have a single bounding box, but depending on how you train your model, you may not even have that one in the ground truth you give to your model), I'd say yes, for the other categories probably not."},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"bygroup = train[['image_id', 'class_name', 'rad_id','class_id']].groupby(['image_id', 'class_name', 'rad_id']).count().reset_index()\nbygroup[['class_name', 'class_id']].groupby(['class_name']).max().sort_values('class_id').rename(columns={'class_id':'Max. number of bounding boxes by one radiologist for an image'})","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"bygroup[['class_name', 'class_id']].groupby(['class_name']).mean().sort_values('class_id').rename(columns={'class_id':'Mean number of boxes per radiologist (once at least one indicated for this class by that radiologist)'}).round(3)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"6\"></a>\n# 6. What is in the .dicom meta-data?\n\nLet's get the meta-data from the dicom files. To me it looks like there might be some value in age and sex, but everything else may not be so useful."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train_path = '../input/vinbigdata-chest-xray-abnormalities-detection/train/'\nfiles = [f for f in os.listdir(train_path) if os.path.isfile(os.path.join(train_path, f))]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"prog = re.compile('^[A-Z]*')\n\ndef get_dcm_contents(file):\n    dcm = Path(train_path + file).dcmread()    \n    properties = [string for string in dir(dcm) if prog.match(string).group(0)!='']\n    dict1 = {'file': file.replace('.dicom', '')}    \n    dict1.update( { what: dcm[what].value for what in properties if isinstance(dcm[what].value, (bytes, bytearray))!=True } )\n    return dict1\n    \ntrain_files = pd.DataFrame( [ get_dcm_contents(file) for file in tqdm(files) ] )","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Something to look at is also the aspect ratio. We can compare it to a 16:9 format that you may know for films - that's a aspect ratio of 1.777 or if you rotate it of 0.5625 - so we see that the average aspect ratio of 0.877 for the X-rays here is less extreme than that, but tends to be taller than wide."},{"metadata":{"trusted":true},"cell_type":"code","source":"train_files['Aspect Ratio'] = train_files['Columns'] / train_files['Rows']\nnp.mean(train_files['Aspect Ratio'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":" Let's save the dataframe as a csv, in case we want to do more with it in other notebooks."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train_files.to_csv('train_dicom_properties.csv.bz2', compression='bz2', index=False)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Now, let's see what we can find in this additional meta-data."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"train_files.describe()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let's have a more detailed look using the [pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) package, which provides a more comprehensive summary than `.describe()`."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"from pandas_profiling import ProfileReport\nprofile = ProfileReport(train_files, title=\"Pandas Profiling Report\")\nprofile.to_widgets()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"A lot of the meta-data seems useless e.g. `PatientWeight` just seems to be missing. I'm also not so sure whether there's any useful information in `PhotometricInterpretation` being MONOCHROME2 for 12357 records and MONOCHROME1 for 2643 records. A lot of other things just have one value, but patients' age and sex may matter as they might tell us something about which diagnoses are more likely."},{"metadata":{},"cell_type":"markdown","source":"It's interesting that there's so much of a mess in the age data in the .dicom files. E.g. the 839 age `000Y` might either mean a missing age or truly an age < 1 years-old. On the other hand does that make sense?"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_files['PatientAge'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"If we think that age `000Y` stands for missing age (and `000D` = 0 days?!, as well), then we can get the list of ages as:"},{"metadata":{"trusted":true},"cell_type":"code","source":"def string_to_float_process(string):\n    if (string=='') | (string=='000D') | (string=='000'):\n        return float(\"NaN\")\n    else:\n        return float(string)\n\nlist_of_ages = [string_to_float_process(string) for string in train_files['PatientAge'].replace(re.compile('Y$'), '') ]\n\nlist_of_ages[0:10]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"For `PatientSex` it looks like there's either `F` or `M`, while `O` appears to indicate a missing value."},{"metadata":{"trusted":true},"cell_type":"code","source":"train_files['PatientSex'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"new7\"></a>\n# 7. Creating fast to read shelve file\n\nOne of the things that will slow us down in the competition is how to read the .dicom files fast. I have a suggestion using `shelve` (like pickle, but allows parallel reading and access via dictionary keys - a persistent dictionary - see the [documentation](https://docs.python.org/3/library/shelve.html)). I resize the images so that the shortest dimension is at least 600 pixels, i.e. the images are probably still larger than you'd use as a model input, but the file we end up saving ends up being small enough for our purposes - primarily through saving the image itself as `uint8` (which hopefully does not loose meaningful information).\n\nThe processing would be too slow for the notebook timeout limit, if I did not use parallel processing. Luckily that easy enough with the `parallel` function of the `fastcore` package.\n\nI'm for the moment retaining the bounding boxes and class labels (and the information on which radiologist `rad_id`) gave this annotation. That way, you can then decide what to do - whether you want to somehow aggregate the annotations or use the different annotations as data augmentations e.g. by using different ones in different epochs."},{"metadata":{"trusted":true},"cell_type":"code","source":"import pydicom\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\nimport albumentations\nimport random\nimport pickle as pkl\nimport fastcore\nfrom fastcore.parallel import parallel\nimport shelve\nimport re\n\ntrain_dir = '../input/vinbigdata-chest-xray-abnormalities-detection/train/'\ntrain = pd.read_csv('../input/vinbigdata-chest-xray-abnormalities-detection/train.csv')\nlist_of_images = np.sort(np.unique(train['image_id'].values))\n\n# Using function from another great notebook: https://www.kaggle.com/raddar/convert-dicom-to-np-array-the-correct-way\ndef read_xray(path, voi_lut = True, fix_monochrome = True):\n    dicom = pydicom.read_file(path)\n    \n    # VOI LUT (if available by DICOM device) is used to transform raw DICOM data to \"human-friendly\" view\n    if voi_lut:\n        data = apply_voi_lut(dicom.pixel_array, dicom)        \n    else:\n        data = dicom.pixel_array\n               \n    # depending on this value, X-ray may look inverted - fix that:\n    if fix_monochrome and dicom.PhotometricInterpretation == \"MONOCHROME1\":\n        data = np.amax(data) - data\n        \n    data = data - np.min(data)\n    data = data / np.max(data)\n    data = (data * 255).astype(np.uint8)\n    data = np.stack([data]*3).transpose(1,2,0)\n        \n    return data\n\n# This function will read a .dicom file, turn the smallest side to 600 pixels, and then save the additional annotations together with the image into a dictionary\ndef get_and_save(x):\n    \n    idx=x[0] \n    image_id=x[1]\n    \n    transform = albumentations.Compose([albumentations.SmallestMaxSize(max_size=600, always_apply=True)], bbox_params=albumentations.BboxParams(format='pascal_voc')) \n    img = read_xray(path=train_dir + image_id + '.dicom')\n    rad_id = np.array([int(re.findall(r'\\d+', rad_id)[0]) for rad_id in train.loc[train['image_id']==image_id, 'rad_id'].values], dtype=np.int8)\n    class_labels = train.loc[train['image_id']==image_id, 'class_id'].values    \n    bboxes = [list(row) for rowid, row in train.loc[train['image_id']==image_id, ['x_min', 'y_min', 'x_max', 'y_max', 'class_id']].fillna({'x_min':0, 'y_min':0, 'x_max':1, 'y_max':1}).astype(np.int16).iterrows() ]\n    \n    transformed = transform(image=img,\n                            bboxes=bboxes, \n                            class_labels=class_labels)\n    \n    return dict(image_id=image_id,\n                image=transformed['image'][:,:,0],\n                rad_id=rad_id,\n                bboxes=np.array(transformed['bboxes'], dtype=np.float32), \n                class_labels=transformed['class_labels'].astype(np.int8))\n    \n# Parallel processing of the .dicom files    \nout1 = parallel(get_and_save, [(idx, image_id) for idx, image_id in enumerate(list_of_images)], n_workers=4, progress=True )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with shelve.open('training_data.db') as myshelf:\n    myshelf.update( { dictentry['image_id']: {'image': dictentry['image'], \n                                              'rad_id': dictentry['rad_id'], \n                                              'bboxes': dictentry['bboxes'], \n                                              'class_labels': dictentry['class_labels'] }  for dictentry in out1 } )","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"How do we access this data e.g. in a data-loader? We can simply use the `image_id` values from `train.csv`, e.g. like this:"},{"metadata":{"trusted":true},"cell_type":"code","source":"with shelve.open('training_data.db', flag='r', writeback=False) as myshelf:\n    print( myshelf['000434271f63a053c4128a0ba6352c7f'] )","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"8new\"></a>\n# 8. Example images of each class\n\nNow, let's look at 3 examples per class that I've selected based on them not having too many other classes involved."},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"train['counter'] = 1\nsummar = train[['image_id', 'rad_id', 'class_id', 'counter']].groupby(['image_id', 'rad_id', 'class_id']).count().reset_index()\\\n    .pivot(index=['image_id', 'rad_id'], columns='class_id', values='counter').fillna(0).reset_index()\\\n    .groupby(['image_id', 'rad_id']).min().astype(np.int).reset_index()\\\n    .groupby('image_id').sum().reset_index()\\\n    .rename(columns={c:f'c{c}' for c in range(15)})\nsummar['totals'] = summar[[f'c{c}' for c in range(15)]].sum(axis=1)\nexample_list = list()\nfor class_id in range(15):    \n    summar['score'] = ((summar['totals']==3) & (summar[f'c{class_id}']==3))*100\\\n        + ((summar['totals']==6) & (summar[f'c{class_id}']==3))*95\\\n        + ((summar['totals']<6) & (summar['totals']>3) & (summar[f'c{class_id}']==3))*90\\\n        + ((summar['totals']==9) & (summar[f'c{class_id}']==3))*85\\\n        + ((summar['totals']<9) & (summar['totals']>6) & (summar[f'c{class_id}']==3))*80\n    summar = summar.sort_values('score', ascending=False) #.reset_index()\n    dalen = len(summar['image_id'].values[0:3])    \n    example_list.append( list(summar['image_id'].values[0:3]))","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"import matplotlib.patches as ptc\nfrom matplotlib.patches import Rectangle\n\ndef get_image(image_id):\n    with shelve.open('training_data.db', \n                     flag='r', writeback=False) as myshelf:\n        tmpdict = myshelf[image_id]                            \n    image = np.stack([tmpdict['image']]*3).transpose(1,2,0)        \n    bboxes = tmpdict['bboxes']\n    class_labels = tmpdict['class_labels']\n    return {'image': image, 'bboxes': bboxes, 'class_labels': class_labels}\n\nfor class_id1 in range(len(example_list)):\n    for image_id in example_list[class_id1]:    \n        im = get_image(image_id)    \n\n        plt.figure(figsize=(20,10));    \n        plt.imshow(im['image']);\n        classname = label_dict[class_id1]\n        plt.suptitle(f'Image {image_id} (example for {class_id1}: {classname})', fontsize=16)\n        ax = plt.gca();\n\n        for bbox, class_id in zip(im['bboxes'], im['class_labels']):\n            #print(f'{image_id}, {rad_id}, {bbox}, {class_id}')        \n            if class_id==class_id1:\n                ecol='r'\n                linestyle = 'dashed'\n                offset=12\n            else:\n                ecol='b'\n                linestyle = 'dotted'\n                offset=24\n\n            # Create a Rectangle patch\n            rect = Rectangle((bbox[0],bbox[1]),bbox[2]-bbox[0],bbox[3]-bbox[1],\n                             linewidth=1,linestyle=linestyle,\n                             edgecolor=ecol,facecolor='none');\n            # Add the patch to the Axes\n            ax.add_patch(rect);\n            ax.annotate(str(class_id) + ': ' + label_dict[class_id], \n                        xy=((bbox[2]+bbox[0])/2, bbox[1]+offset), \n                        xycoords='data', color=ecol);","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"7\"></a>\n# 9. Some thoughts on cross-validation\n\nAs always, the right validation strategy is important. We would not want to have multiple instances of the same image in the training and validation fold, so we should cross-validate images to either be in the training for or the validation fold. It's easiest to do that with the filenames of the images (or the unique `image_id` values), but group-K-fold grouped by `image_id` would achieve that on the **train.csv** file.\n\nAnother consideration is whether to use e.g. multi-label stratified cross-validation to keep the distribution of labels present about the same across folds."},{"metadata":{},"cell_type":"markdown","source":"<a id=\"8\"></a>\n# 10. Some initial thoughts on data augmentation\n\nOne interesting challenge here is how to deal with these bounding boxes in data augmentations - I assume there's plenty of past solutions in this type of setting that figure out how to do this, but note that the `albumentations` package does come with the ability to transform masks on images along with the image. However, how to do augmentations (i.e. if we rotate, it is no longer a box with boundaries parallel to the image edges)? Presumably there's experience with models like YOLO or EfficientDet that show how to do this?\n\nSome ideas for what `albumentations` augmentations might be reasonable (i.e. not really change the meaning of the image as interpreted by humans, which tends to be pretty save in terms of augmentations):\n\n* `RandomResizedCrop`: Makes sense, but we probably want to make sure the whole chest areas is visible, so we may want to use the `scale=(0.9, 1.0)` option (replacing the default `scale=(0.08, 1.0)`)\n* `ShiftScaleRotate(rotate_limit=10)`: How a person is X-rayed can obviously vary a bit and they might lean a bit either way, so this is a totally plausible augmentation, but I'd consider limiting the rotation as shown.\n* `RandomBrightnessContrast`: Some variation in brightness is only natural.\n* `CoarseDropout`: drops-out rectangular regions in the image - this is not really something you'd see in a real x-ray, but not seeing a small area should usually not change your interpretation, so it makes sense.\n\n\nLess obvious, but perhaps useful (hey, you need to test it): `albumentations.Transpose(p=0.5)`, `albumentations.HorizontalFlip(p=0.5)` and `albumentations.VerticalFlip(p=0.5)` all result in images that do not look like a real X-ray would look like, but if it helps the neural network to learn, perhaps it's okay to use these (cross-validate and see, I suppose).\n\nHow much augmentations like `HueSaturationValue` here, given that the images are black and white, I'm not clear on and it may depend on what you do with colors to increase contrast. `ISONoise` (i.e. doing something like camera noise) is often helpful, but these are not normal camera pictures, so who knows how much it would help, but I thought it might be worth trying.\n\nObviously, there's way more options in the [documentation](https://albumentations.ai/docs/api_reference/) that you may wish to consider like blurring and so on. More advanced approaches like CutMix or SnapMix might also be interesting.\n\nImplementing augmentations with albumentations can look like this:"},{"metadata":{"trusted":true},"cell_type":"code","source":"import albumentations\n\nINPUT_SHAPE = 224 # Whatever input you want to put into your neural network\n\naugs = albumentations.Compose([\n            albumentations.SmallestMaxSize(max_size=600),\n            albumentations.RandomResizedCrop(INPUT_SHAPE, INPUT_SHAPE, scale=(0.9, 1.0)),            \n            albumentations.ShiftScaleRotate(rotate_limit=10, p=0.5),\n            albumentations.RandomBrightnessContrast(\n                brightness_limit=(-0.1,0.1), \n                contrast_limit=(-0.1, 0.1), \n                p=0.5\n            ),\n            albumentations.ISONoise(),\n            albumentations.CoarseDropout(p=0.5, max_holes=9),\n            albumentations.CoarseDropout(p=0.5, max_width=16, max_height=25, max_holes=3, fill_value=0),\n            albumentations.CoarseDropout(p=0.5, max_width=16, max_height=25, max_holes=3, fill_value=255)\n])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"I'm using the approach from this [notebook](https://www.kaggle.com/raddar/convert-dicom-to-np-array-the-correct-way) - with the minor change for turning it into a 3-channel RGB image via `np.stack` which I added - so go there and upvote it, too."},{"metadata":{"trusted":true},"cell_type":"code","source":"import pydicom\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\n\ndef read_xray(path, voi_lut = True, fix_monochrome = True):\n    dicom = pydicom.read_file(path)\n    \n    # VOI LUT (if available by DICOM device) is used to transform raw DICOM data to \"human-friendly\" view\n    if voi_lut:\n        data = apply_voi_lut(dicom.pixel_array, dicom)\n    else:\n        data = dicom.pixel_array\n               \n    # depending on this value, X-ray may look inverted - fix that:\n    if fix_monochrome and dicom.PhotometricInterpretation == \"MONOCHROME1\":\n        data = np.amax(data) - data\n        \n    data = data - np.min(data)\n    data = data / np.max(data)\n    data = (data * 255).astype(np.uint8)\n    data = np.stack([data]*3).transpose(1,2,0)\n        \n    return data\n\nimg = read_xray('../input/vinbigdata-chest-xray-abnormalities-detection/train/0108949daa13dc94634a7d650a05c0bb.dicom')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"We are now going to take this one image and then look at a bunch of augmentations of it."},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize = (12,12));\nplt.imshow(img, 'gray');","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"So, let's see lots of augmented versions of this image:"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, axes = plt.subplots(nrows=10, ncols=4, figsize=(20,60))\n\nfor idx in range(40):\n    row = idx // 4\n    col = idx % 4\n    axes[row, col].axis(\"off\")\n    axes[row, col].imshow(augs(image=img)['image'], cmap=\"gray\", aspect=\"auto\")\nplt.subplots_adjust(wspace=.05, hspace=.05)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"As you can see, we get a nice variation in augmented images. Whether you think that's too much augmentation or not, is of course up to you."},{"metadata":{},"cell_type":"markdown","source":"<a id=\"9\"></a>\n# 11. Implementing augmentations with bounding boxes\n\nWe will also, of course, have to make sure the augmentations take into account where the bounding boxes are. Luckily, the `albumentations` package covers [this](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/). We are getting the bounding boxes in the format that `albumentations` calls `pascal_voc`. We tell `albumentations` this as shown below with the `bbox_params` parameter. I'm leaving out `CoarseDropout` here, because it looks like it cannot deal with bounding boxes, but to me it would make sense to apply the augmentation and leave the bounding boxes as is (as long as we make sure the cut-outs are clearly smaller than the bounding boxes)."},{"metadata":{"trusted":true},"cell_type":"code","source":"augs = albumentations.Compose([\n            albumentations.SmallestMaxSize(max_size=600),\n            albumentations.RandomResizedCrop(INPUT_SHAPE, INPUT_SHAPE, scale=(0.9, 1.0)),            \n            albumentations.ShiftScaleRotate(rotate_limit=10, p=0.5),\n            albumentations.RandomBrightnessContrast(\n                brightness_limit=(-0.1,0.1), \n                contrast_limit=(-0.1, 0.1), \n                p=0.5\n            ),\n            albumentations.ISONoise()], \n    bbox_params=albumentations.BboxParams(format='pascal_voc'))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"So, let's apply this augmentation to the image we used above, for the classifications assigned by radiologist (`rad_id`) `R8`."},{"metadata":{"trusted":true},"cell_type":"code","source":"train[train['image_id']=='0108949daa13dc94634a7d650a05c0bb']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"The `pascal_voc` format foresees the bounding boxes to be given as `x_min`, `y_min`, `x_max` and `y_max`, and it is recommended to give class-labels via the `class_labels` parameters."},{"metadata":{"trusted":true},"cell_type":"code","source":"transformed = augs(image=img, \n                  bboxes=[[772, 1373, 1781, 1753, 3],\n                          [1103, 732, 1330, 1021, 0],], \n                  class_labels=[3, 0]) # Could also provide labels as strings\n\n\nplt.figure(figsize = (8,8));\nplt.imshow(transformed['image'], cmap=\"gray\", aspect=\"auto\");","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"And this is what the bounding boxes look like after the augmentation"},{"metadata":{"trusted":true},"cell_type":"code","source":"transformed['bboxes']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"transformed['class_labels']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"I hope this is useful for creating your own dataloader."},{"metadata":{},"cell_type":"markdown","source":"<a id=\"11a\"></a>\n# 12.Possible data issues\nAs we noted above, there's patients with multiple annotations by the same radiologist of cardiomegaly and aortic enlargment, which seems odd. Let's plot these cases. Red squares are cardiomegaly, yellow is aortic enlargment and blue is for any other diagnoses."},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"train = pd.read_csv('../input/vinbigdata-chest-xray-abnormalities-detection/train.csv')\nbygroup = train[['image_id', 'class_name', 'rad_id','class_id']].groupby(['image_id', 'class_name', 'rad_id']).count().reset_index()\nstrange_cases = bygroup.loc[ [ (cn[0] in ['Aortic enlargement', 'Cardiomegaly']) & cn[1]  for cn in zip(bygroup['class_name'].values, list(bygroup['class_id']==2)) ], ['image_id', 'rad_id']]\nstrange_cases = pd.merge(train, strange_cases, on=['image_id', 'rad_id'], how='inner').sort_values(['image_id', 'rad_id', 'class_name']).reset_index(drop=True)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"from matplotlib.patches import Rectangle\n\ndef get_image(image_id, rad_id):\n    with shelve.open('training_data.db', flag='r', writeback=False) as myshelf:\n        tmpdict = myshelf[image_id]                        \n    which_indices = [idx for idx, val in enumerate(tmpdict['rad_id']) if val==rad_id]\n    image = np.stack([tmpdict['image']]*3).transpose(1,2,0)        \n    bboxes = tmpdict['bboxes'][which_indices]\n    class_labels = tmpdict['class_labels'][which_indices]        \n    return {'image': image, 'bboxes': bboxes, 'class_labels': class_labels}\n\nfor index, row in strange_cases[['image_id', 'rad_id']].drop_duplicates().reset_index(drop=True).iterrows():        \n    image_id = row['image_id']\n    rad_id = int(re.findall(r'\\d+', row['rad_id'])[0])\n    im = get_image(image_id, rad_id)\n\n    plt.figure(figsize=(20,10));    \n    plt.imshow(im['image']);\n    plt.suptitle(f'Image {image_id}, radiologist {rad_id}', fontsize=16)    \n    ax = plt.gca();\n\n    for bbox, class_id in zip(im['bboxes'], im['class_labels']):\n        #print(f'{image_id}, {rad_id}, {bbox}, {class_id}')        \n        if class_id==0: \n            ecol='y'\n        elif class_id==3: \n            ecol='r'\n        else:\n            ecol='b'\n\n        # Create a Rectangle patch\n        rect = Rectangle((bbox[0],bbox[1]),bbox[2]-bbox[0],bbox[3]-bbox[1],linewidth=1,edgecolor=ecol,facecolor='none');\n        # Add the patch to the Axes\n        ax.add_patch(rect);","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"I've also created [another notebook](https://www.kaggle.com/bjoernholzhauer/finding-data-issues-and-mislabeled-bounding-boxes) where I try to find a wider variety of data issues in a more systematic fashion."},{"metadata":{},"cell_type":"markdown","source":"<a id=\"10\"></a>\n# 13. What are we asked to predict, exactly?"},{"metadata":{},"cell_type":"markdown","source":"For each image, we must predict class ID, confidence score, and a bounding box in the same way as in the training data. However, the prediction does get turned into a string (order for coordinates: xmin ymin xmax ymax).\n\nIf we predict that there are no classes (=class 14) in a given image, we are asked to predict 14 1.0 0 0 1 1, 1.0 is the confidence, and 0 0 1 1 is a one-pixel bounding box. See also [the competition page](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection/overview/evaluation). The sample submission only seems to show that case."},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.read_csv('../input/vinbigdata-chest-xray-abnormalities-detection/sample_submission.csv')","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}