{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Introduction\nWhen you have a broken arm, radiologists help save the day—and the bone. These doctors diagnose and treat medical conditions using imaging techniques like CT and PET scans, MRIs, and, of course, X-rays. Yet, as it happens when working with such a wide variety of medical tools, radiologists face many daily challenges, perhaps the most difficult being the chest radiograph. The interpretation of chest X-rays can lead to medical misdiagnosis, even for the best practicing doctor. Computer-aided detection and diagnosis systems (CADe/CADx) would help reduce the pressure on doctors at metropolitan hospitals and improve diagnostic quality in rural areas.\n\nExisting methods of interpreting chest X-ray images classify them into a list of findings. There is currently no specification of their locations on the image which sometimes leads to inexplicable results. A solution for localizing findings on chest X-ray images is needed for providing doctors with more meaningful diagnostic assistance.","metadata":{}},{"cell_type":"code","source":"import os\nimport glob\nimport numpy as np\nimport pandas as pd\n\ndata = pd.read_csv('../input/vinbigdata-chest-xray-abnormalities-detection/train.csv')\ndata","metadata":{"execution":{"iopub.status.busy":"2021-08-12T03:52:01.587522Z","iopub.execute_input":"2021-08-12T03:52:01.587944Z","iopub.status.idle":"2021-08-12T03:52:01.863577Z","shell.execute_reply.started":"2021-08-12T03:52:01.587902Z","shell.execute_reply":"2021-08-12T03:52:01.862563Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class_names = data.sort_values(by=['class_id']).class_name.unique()\nclass_dict = pd.Series(class_names).to_dict()\nclass_dict","metadata":{"execution":{"iopub.status.busy":"2021-08-12T04:04:36.504946Z","iopub.execute_input":"2021-08-12T04:04:36.50534Z","iopub.status.idle":"2021-08-12T04:04:36.53271Z","shell.execute_reply.started":"2021-08-12T04:04:36.505307Z","shell.execute_reply":"2021-08-12T04:04:36.531749Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Work in Progress","metadata":{}}]}