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      "title": "notebook10f0c2885a",
      "source": "live"
    },
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      "ref": "angeloespinoza/tasks-to-do",
      "title": "Tasks-to-do",
      "source": "live"
    },
    {
      "ref": "yollotltamayo/gleason-densenet",
      "title": "gleason_densenet",
      "source": "live"
    },
    {
      "ref": "mdmahirlabib/panda-keras-baseline",
      "title": "PANDA keras baseline",
      "source": "live"
    },
    {
      "ref": "vsend123/nuclei-segmentation",
      "title": "nuclei Segmentation",
      "source": "live"
    },
    {
      "ref": "ironwing/panda-challenge",
      "title": "panda challenge",
      "source": "live"
    },
    {
      "ref": "mewtyunjay/finalproject-efficientnet-b0",
      "title": "FinalProject EfficientNet-B0",
      "source": "live"
    },
    {
      "ref": "wemakeai/prostate-cancer-fastai-baseline-model",
      "title": "prostate_cancer_fastai_baseline_model",
      "source": "live"
    },
    {
      "ref": "wemakeai/prostate-cancer",
      "title": "prostate_cancer",
      "source": "live"
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    {
      "ref": "marshath/tackling-panda-using-fastai-v2",
      "title": "Tackling PANDA using fastai v2",
      "source": "live"
    },
    {
      "ref": "blablamc/panda-inference",
      "title": "PANDA Inference",
      "source": "live"
    },
    {
      "ref": "vsend123/image-analysis",
      "title": "Image Analysis",
      "source": "live"
    },
    {
      "ref": "joangibert/panda-tile-pooling-fastai2",
      "title": "PANDA_tile_pooling_fastai2",
      "source": "live"
    },
    {
      "ref": "habibmrad1983/panda-concat-tile-pooling-starter-0-79-lb",
      "title": "PANDA concat tile pooling starter [0.79 LB]",
      "source": "live"
    },
    {
      "ref": "habibmrad1983/prostate-cancer-in-depth-understanding-eda-model",
      "title": "Prostate Cancer: In Depth Understanding,EDA ,Model",
      "source": "live"
    },
    {
      "ref": "habibmrad1983/panda-eda-better-visualization-simple-baseline",
      "title": "PANDA - EDA + Better Visualization+Simple Baseline",
      "source": "live"
    },
    {
      "ref": "umashankar01/vgg-19",
      "title": "VGG 19",
      "source": "live"
    },
    {
      "ref": "rmicrobe/panda-challenge-my-first-submission",
      "title": "PANDA Challenge: My First Submission",
      "source": "live"
    },
    {
      "ref": "rmicrobe/train-and-create-test-pkl-file",
      "title": "Train and Create Test.pkl File",
      "source": "live"
    },
    {
      "ref": "micheomaano/code-for-encoding-and-decoding",
      "title": "Code_for_Encoding_and_Decoding",
      "source": "live"
    },
    {
      "ref": "luckass/stage-kanker",
      "title": "Stage Kanker",
      "source": "live"
    },
    {
      "ref": "rmicrobe/creating-training-dataset",
      "title": "Creating Training Dataset",
      "source": "live"
    },
    {
      "ref": "anaselmasry/tensorflow-cnn-data-augmentation-prostate-cancer",
      "title": "TensorFlow CNN, Data Augmentation: Prostate Cancer",
      "source": "live"
    },
    {
      "ref": "umashankar01/xception-final",
      "title": " Xception Final",
      "source": "live"
    },
    {
      "ref": "umashankar01/densenet121-final",
      "title": "DenseNet121 Final",
      "source": "live"
    },
    {
      "ref": "saintyemix1/my-project-on-prostate-cancer",
      "title": "My Project on Prostate cancer",
      "source": "live"
    },
    {
      "ref": "umashankar01/final-project",
      "title": "Final project",
      "source": "live"
    },
    {
      "ref": "hazimirfan/cb-stem20-project-amna-hazim-v2",
      "title": "CB STEM20 Project-Amna & Hazim v2",
      "source": "live"
    },
    {
      "ref": "ayeshasaqib/final-project",
      "title": "Final project",
      "source": "live"
    },
    {
      "ref": "ahmadzafar7484/ahmadzafar-finalproject",
      "title": "Ahmadzafar-finalproject",
      "source": "live"
    },
    {
      "ref": "kwjcndocjn/panda-competition-in-tensorlfow",
      "title": "PANDA competition in Tensorlfow",
      "source": "live"
    },
    {
      "ref": "umashankar01/efficientnet-b7-final",
      "title": "EfficientNet B7 Final",
      "source": "live"
    },
    {
      "ref": "umashankar01/final-res-vgg-incep",
      "title": "Final-res-vgg-incep",
      "source": "live"
    },
    {
      "ref": "jackbyte/sample-gleason-biopsy-pictures",
      "title": "Sample Gleason Biopsy Pictures",
      "source": "live"
    },
    {
      "ref": "podkowa/new-classification-panda-eff-intermediate-fold1",
      "title": "new-classification-panda-eff-intermediate-fold1",
      "source": "live"
    },
    {
      "ref": "eddiefroufrou/arutema-base-model-w-logits-a-s-36x128x128",
      "title": "Arutema base model w logits - a&s_36x128x128",
      "source": "live"
    },
    {
      "ref": "spears27/panda-final-inference",
      "title": "PANDA_Final_Inference",
      "source": "live"
    },
    {
      "ref": "aathiraks/exp5-inference-notebook-d766a4",
      "title": "exp5_Inference notebook d766a4",
      "source": "live"
    },
    {
      "ref": "tikoboss/create-tfrecords-fpr-prostate-cancer-grade",
      "title": "Create_TFRecords_fpr_prostate-cancer-grade",
      "source": "live"
    },
    {
      "ref": "jackbyte/panda-challenge-failed-successfully",
      "title": "PANDA Challenge failed successfully",
      "source": "live"
    },
    {
      "ref": "rahulmunet1206/panda-inference-w-36-tiles-256",
      "title": "PANDA Inference w/ 36 tiles_256",
      "source": "live"
    },
    {
      "ref": "spears27/prostate-cancer-efnetb3-fastai-image-augmentation",
      "title": "Prostate Cancer EfnetB3| fastai Image augmentation",
      "source": "live"
    },
    {
      "ref": "spears27/panda-fastai-inference",
      "title": "PANDA_FASTAI_Inference",
      "source": "live"
    },
    {
      "ref": "flyingmuttus/panda-independence-eds-inference",
      "title": "PANDA_independence_EDS_inference",
      "source": "live"
    },
    {
      "ref": "spears27/prostate-cancer-efnetb3-fastai-custom-datablock",
      "title": "Prostate Cancer EfnetB3| fastai | Custom DataBlock",
      "source": "live"
    },
    {
      "ref": "anirbannag/panda-checkpoint-demo",
      "title": "PANDA_checkpoint_demo",
      "source": "live"
    },
    {
      "ref": "anirbannag/panda-checkpoint",
      "title": "PANDA checkpoint",
      "source": "live"
    },
    {
      "ref": "mariajahan/train-efficientnet-b0-w-36-tiles-256-lb0-87",
      "title": "Train EfficientNet-B0 w/ 36 tiles_256 [LB0.87]",
      "source": "live"
    },
    {
      "ref": "jotaporras/edwin-submission-1-copy",
      "title": "Edwin submission_1 Copy",
      "source": "live"
    },
    {
      "ref": "qitvision/jj-2020-07-21-all-models-cleaned",
      "title": "JJ-2020-07-21_all_models (cleaned)",
      "source": "live"
    },
    {
      "ref": "ctrasd123/panda-singlenet-submit",
      "title": "panda-singlenet-submit",
      "source": "live"
    },
    {
      "ref": "natalyayurina/kernel4a3b468030",
      "title": "kernel4a3b468030",
      "source": "live"
    },
    {
      "ref": "bbanuprasad/designing-classifier-1",
      "title": "Designing classifier 1",
      "source": "live"
    },
    {
      "ref": "kyoshioka47/5-fold-effb0-with-cleaned-labels-pb-0-935",
      "title": "5-fold EffB0 with cleaned labels [PB 0.935]",
      "source": "live"
    },
    {
      "ref": "hernoo/submission-panda",
      "title": "submission_PANDA",
      "source": "live"
    },
    {
      "ref": "debanik123/p-keras-vgg16",
      "title": "P_Keras VGG16",
      "source": "live"
    },
    {
      "ref": "kyoshioka47/late-famrepro-fam-reproaru-ensemble-0725",
      "title": "late_famrepro_fam+reproaru_ensemble_0725",
      "source": "live"
    },
    {
      "ref": "hernoo/tile-pre-processing",
      "title": "tile_pre_processing",
      "source": "live"
    },
    {
      "ref": "tikoboss/skin-cancer-siim-melanoma",
      "title": "SKIN_CANCER_SIIM_MELANOMA",
      "source": "live"
    },
    {
      "ref": "hernoo/panda-resnet50",
      "title": "PANDA_resnet50",
      "source": "live"
    },
    {
      "ref": "qitvision/panda-r-hm-ai-private-score-0-93",
      "title": "PANDA_rähmä.ai [Private score 0.93]",
      "source": "live"
    },
    {
      "ref": "dararc/prostate-cancer-detection-end-to-end-ml-project",
      "title": "Prostate Cancer Detection - End to End ML project.",
      "source": "live"
    },
    {
      "ref": "coreacasa/12th-place-solution-inference-notebook",
      "title": "12th Place Solution - Inference Notebook",
      "source": "live"
    },
    {
      "ref": "coreacasa/12th-place-solution-quick-save-inference",
      "title": "12th Place Solution - Quick Save Inference",
      "source": "live"
    },
    {
      "ref": "utkarshtripathi/panda-inference-efficientnet-b1",
      "title": "Panda Inference EfficientNet-b1 ",
      "source": "live"
    },
    {
      "ref": "soumya25/pratyabhij-a-na-3",
      "title": "Pratyabhijñāna_3",
      "source": "live"
    },
    {
      "ref": "shentao/fork-of-panda-inference-final-ave",
      "title": "Fork of PANDA-Inference-final-ave",
      "source": "live"
    },
    {
      "ref": "rsinda/panda-inference-efficientnet-b1",
      "title": "Panda Inference EfficientNet-b1 ",
      "source": "live"
    },
    {
      "ref": "tikoboss/kernel5a15dde37e",
      "title": "kernel5a15dde37e",
      "source": "live"
    },
    {
      "ref": "kyunghoonhur/efficientnet-b0-b1-1-1-ensemble-0-92664",
      "title": "Efficientnet-B0, B1 1:1 Ensemble [0.92664]",
      "source": "live"
    },
    {
      "ref": "sankarsanseal/images-and-masks-at-lowest-resolution",
      "title": "Images_and_Masks_at_Lowest_Resolution",
      "source": "live"
    },
    {
      "ref": "iafoss/panda-init-class-128-voting",
      "title": "Panda init class 128 voting",
      "source": "live"
    },
    {
      "ref": "fifantor50/create-tfrecords-fpr-prostate-cancer-grade",
      "title": "Create_TFRecords_fpr_prostate-cancer-grade",
      "source": "live"
    },
    {
      "ref": "blablamc/panda-training-with-custom-dataset",
      "title": "PANDA Training With Custom Dataset",
      "source": "live"
    },
    {
      "ref": "dgrechka/panda-submit-ensemble-37c-40c-exp-models",
      "title": "PANDA: submit ensemble (37c, 40c exp models)",
      "source": "live"
    },
    {
      "ref": "malyshevvalery/tilewise-pipeline",
      "title": "Tilewise pipeline",
      "source": "live"
    },
    {
      "ref": "qitvision/jj-2020-07-21-all-models",
      "title": "JJ-2020-07-21_all_models",
      "source": "live"
    },
    {
      "ref": "rvslight/mean-ensemble-asnell-and-shujun-by-jjshadow",
      "title": "Mean ensemble Asnell and Shujun by JJShadow",
      "source": "live"
    },
    {
      "ref": "vineeth1999/prostate-cancer-grade-assessment-panda-challenge",
      "title": "Prostate cANcer graDe Assessment (PANDA) Challenge",
      "source": "live"
    },
    {
      "ref": "jakobw/panda-submit-clas-twodl",
      "title": "panda_submit_clas_twoDL",
      "source": "live"
    },
    {
      "ref": "tikoboss/panda-densenet-keras-starter-tpu-563d0d",
      "title": "PANDA DenseNet Keras Starter TPU 563d0d",
      "source": "live"
    },
    {
      "ref": "arroqc/tile-model-ensemble",
      "title": "Tile Model Ensemble",
      "source": "live"
    },
    {
      "ref": "dararc/gl3-panda-training",
      "title": "gl3 PANDA training",
      "source": "live"
    },
    {
      "ref": "drhabib/ens-xie-2fold-drhb-igor-ru-se50-ru-efnet-5",
      "title": "(ENS_XIE_2FOLD_DRHB_IGOR_RU_SE50_RU_EFNET)/5",
      "source": "live"
    },
    {
      "ref": "digvijayyadav/panda-inference-w-36-tiles-256",
      "title": "PANDA Inference w/ 36 tiles_256",
      "source": "live"
    },
    {
      "ref": "nikhilbartwal001/prostate-cancer-detection-system",
      "title": "Prostate Cancer Detection System",
      "source": "live"
    },
    {
      "ref": "darraghdog/lstm-effnetb2-1507v33a",
      "title": "lstm_effnetb2_1507v33A",
      "source": "live"
    },
    {
      "ref": "blablamc/panda-by-egor",
      "title": "PANDA BY EGOR",
      "source": "live"
    },
    {
      "ref": "nikhilbartwal001/panda-inference",
      "title": "PANDA Inference",
      "source": "live"
    },
    {
      "ref": "rajnishe/panda-tpu-png-1536-fine-rune-fold3",
      "title": "PANDA TPU PNG(1536)-fine-rune-fold3",
      "source": "live"
    },
    {
      "ref": "ssaisuryateja/prostrate-cancer",
      "title": "Prostrate_Cancer",
      "source": "live"
    },
    {
      "ref": "blablamc/panda-custom-dataset",
      "title": "PANDA Custom Dataset ",
      "source": "live"
    },
    {
      "ref": "micheomaano/landmark-metric-learning",
      "title": "Landmark Metric Learning",
      "source": "live"
    },
    {
      "ref": "akashsuper2000/tensorflow-with-tpus",
      "title": "Tensorflow With TPUs",
      "source": "live"
    },
    {
      "ref": "dragonzhang/mystudy-kernel",
      "title": "mystudy_kernel",
      "source": "live"
    },
    {
      "ref": "mawanda/cropped-dataset-optimized-for-kaggle",
      "title": "Cropped dataset optimized for Kaggle",
      "source": "live"
    },
    {
      "ref": "sicmunduscreatusest/red-panda-lb-0-76",
      "title": "Red Panda [LB: 0.76]",
      "source": "live"
    },
    {
      "ref": "feascr/panda-custom-dataset",
      "title": "PANDA Custom Dataset ",
      "source": "live"
    },
    {
      "ref": "shubham9455999082/simple-opencv-cnn-vg16",
      "title": "simple opencv cnn, vg16",
      "source": "live"
    },
    {
      "ref": "rajnishe/panda-tpu-png-1536-full-dataset",
      "title": "PANDA TPU PNG(1536)_full_dataset",
      "source": "live"
    },
    {
      "ref": "arpcode/fork-of-pandas-and-bamboos-learning-rate-schedul",
      "title": "Fork of Pandas and Bamboos - Learning Rate Schedul",
      "source": "live"
    },
    {
      "ref": "nikhilbartwal001/coords-precomp",
      "title": "Coords precomp",
      "source": "live"
    },
    {
      "ref": "snide713/basic-pipeline-for-pytorchers",
      "title": "Basic Pipeline for Pytorchers",
      "source": "live"
    },
    {
      "ref": "timsthebomb/test-submission",
      "title": "Test Submission",
      "source": "live"
    },
    {
      "ref": "dextrousjinx/fast-ai-lecture-1-practical",
      "title": "Fast AI - Lecture 1 Practical",
      "source": "live"
    },
    {
      "ref": "akashsuper2000/tpu-tensorflow-42x256x256x3",
      "title": "TPU Tensorflow 42x256x256x3",
      "source": "live"
    },
    {
      "ref": "rajnishe/rc-panda-xgboost-stacking",
      "title": "RC-panda-XGBoost-Stacking",
      "source": "live"
    },
    {
      "ref": "akashsuper2000/pandas-42x256x256x3-inference",
      "title": "Pandas 42x256x256x3 Inference",
      "source": "live"
    },
    {
      "ref": "imakaruamikurah/keras-densenet121-prostate-cancer",
      "title": "Keras DenseNet121 - Prostate Cancer",
      "source": "live"
    },
    {
      "ref": "arpcode/pandas-and-bamboos-learning-rate-scheduling",
      "title": "Pandas and Bamboos - Learning Rate Scheduling",
      "source": "live"
    },
    {
      "ref": "tejask98/blend-efficientnet-b0-and-b1",
      "title": "Blend EfficientNet b0 and b1",
      "source": "live"
    },
    {
      "ref": "takashinemoto/train-efficientnet-b3-w-36-tiles-256",
      "title": "Train_EfficientNet-B3_w-36_tiles-256",
      "source": "live"
    },
    {
      "ref": "c7934597/prostate-cancer-grade-inference-panda",
      "title": "Prostate Cancer Grade Inference (PANDA)",
      "source": "live"
    },
    {
      "ref": "tachyon777/panda-tachyon-introduction",
      "title": "Panda_tachyon_introduction",
      "source": "live"
    },
    {
      "ref": "dararc/eda-on-tile-labels",
      "title": "EDA on tile labels",
      "source": "live"
    },
    {
      "ref": "mahmudds/prostate-cancer-grade-assessment-panda-challenge",
      "title": "Prostate cANcer graDe Assessment (PANDA) Challenge",
      "source": "live"
    },
    {
      "ref": "rajnishe/tta-implementation",
      "title": "TTA_implementation",
      "source": "live"
    },
    {
      "ref": "arunadeviramesh/cancer-grade-assessment",
      "title": "Cancer grade assessment",
      "source": "live"
    },
    {
      "ref": "nizamuddin/panda-generating-data-2000",
      "title": "PANDA generating data_2000",
      "source": "live"
    },
    {
      "ref": "mayankkrawat/cancer",
      "title": "Cancer",
      "source": "live"
    },
    {
      "ref": "dragonsan17/save-good-tiles-0",
      "title": "save_good_tiles_0",
      "source": "live"
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    {
      "ref": "naomiding/panda-tiles36x256x256-stain-norm-downsz128-model-1",
      "title": "panda_tiles36x256x256_stain_norm_downsz128_model_1",
      "source": "live"
    },
    {
      "ref": "mdp1990/prostate-cancer-challenge-eda",
      "title": "Prostate Cancer Challenge- EDA ",
      "source": "live"
    },
    {
      "ref": "timsthebomb/fastaiv2-resnet34-dynamic-unet",
      "title": "Fastaiv2 Resnet34 Dynamic Unet",
      "source": "live"
    },
    {
      "ref": "mdp1990/prostate-cancer-challenge-model-building",
      "title": "Prostate Cancer Challenge- Model Building ",
      "source": "live"
    },
    {
      "ref": "amacpherson88/generate-list-of-random-tiles-as-csv",
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      "source": "live"
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    {
      "ref": "ghaiyur/ensemble-model",
      "title": "Ensemble Model",
      "source": "live"
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    {
      "ref": "dlarionov/tile-factory",
      "title": "tile factory",
      "source": "live"
    },
    {
      "ref": "micheomaano/pandas-42x256x256x3-inference",
      "title": "Pandas 42x256x256x3 Inference",
      "source": "live"
    },
    {
      "ref": "micheomaano/tpu-training-tensorflow-iafoos-method-42x256x256x3",
      "title": "TPU Training Tensorflow Iafoos Method 42x256x256x3",
      "source": "live"
    },
    {
      "ref": "duccongduong/15epoch-efficientnetb3-tts-inference",
      "title": "15epoch_EfficientNetB3_tts_inference",
      "source": "live"
    },
    {
      "ref": "tikoboss/prostate-cancer",
      "title": "##Prostate-Cancer##",
      "source": "live"
    },
    {
      "ref": "jakobw/data-augmentation-test",
      "title": "data augmentation test",
      "source": "live"
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    {
      "ref": "kangkangkk/panda-concat-tile-pooling-starter-0-79-lb",
      "title": "PANDA concat tile pooling starter [0.79 LB]",
      "source": "live"
    },
    {
      "ref": "fadzlinrafi/starter-turning-image-tiles-into-pandas-df",
      "title": "Starter: Turning image tiles into Pandas DF",
      "source": "live"
    },
    {
      "ref": "micheomaano/medium-resolution-dataset-48x256x256",
      "title": "Medium_resolution_dataset_48x256x256",
      "source": "live"
    },
    {
      "ref": "iafoss/panda-128-tiles",
      "title": "Panda 128 tiles",
      "source": "live"
    },
    {
      "ref": "micimize/complex-packing",
      "title": "complex_packing",
      "source": "live"
    },
    {
      "ref": "deemocean/prostate",
      "title": "Prostate",
      "source": "live"
    },
    {
      "ref": "prachi1211/eda-wsi-pixels",
      "title": "EDA - WSI Pixels ",
      "source": "live"
    },
    {
      "ref": "thomasx/test-the-big-size",
      "title": "Test the big size",
      "source": "live"
    },
    {
      "ref": "razamh/panda-concat-tile-pooling-starter-0-79-lb",
      "title": "PANDA concat tile pooling starter [0.79 LB]",
      "source": "live"
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      "ref": "raghaw/panda-medium-resolution-dataset-25x256x256",
      "title": "PANDA_medium_resolution_dataset_25x256x256",
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      "ref": "jaideepvalani/pandas-resnext-patching-method",
      "title": "Pandas_resnext_patching_method",
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      "ref": "vainof/panda-inference-w-36-tiles-256",
      "title": "PANDA Inference w/ 36 tiles_256",
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      "ref": "rajnishe/rc-panda-ensemble-twin-backbone",
      "title": "RC_Panda-Ensemble-Twin-Backbone",
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      "ref": "takashinemoto/panda-inference-w-36-tiles-256",
      "title": "PANDA Inference w/ 36 tiles_256",
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      "ref": "cathyyan37/prostate-cancer-grade-assessment-panda-challenge",
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      "ref": "apurvasharma866/panda-challenge",
      "title": "PANDA_Challenge",
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      "ref": "mudasserafzal/prostate-cancer-segmentation",
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      "ref": "rajnishe/rc-panda-regression-th-finder",
      "title": "RC_Panda Regression-th-finder",
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      "ref": "avirdee/fastai2-balanced-stratified-submission",
      "title": "Fastai2 | Balanced | Stratified | Submission ✔",
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      "ref": "kartikkhandelwal/panda-data-prepocessing",
      "title": "PANDA: Data Prepocessing",
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      "ref": "tottinfish/for-test-submission",
      "title": "For-test&submission",
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      "title": "[PANDA] Optimized tiling (+ tf.data.Dataset)",
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      "ref": "rajnishe/rc-panda-xception-on-level-2-regressionmodel",
      "title": "RC_Panda Xception on Level-2-RegressionModel",
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      "ref": "tasnimnishatislam/panda-resize-and-save-train-data",
      "title": "PANDA: Resize and Save Train Data",
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      "ref": "naomidd/panda-stain-norm-downsample12x64x64-inference",
      "title": "PANDA stain norm downsample12x64x64 inference",
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      "ref": "mawanda/qwk-metric-and-loss-in-pytorch",
      "title": "QWK metric and loss in PyTorch",
      "source": "live"
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      "ref": "amyjang/tensorflow-cnn-data-augmentation-prostate-cancer",
      "title": "TensorFlow CNN, Data Augmentation: Prostate Cancer",
      "source": "live"
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      "ref": "naomiding/panda-stain-norm-12x128x128-inference",
      "title": "PANDA stain norm 12x128x128 inference",
      "source": "live"
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      "ref": "naomiding/panda-concat-tile-pooling-w-stain-norm-12x128x128",
      "title": "PANDA concat tile pooling w/stain norm 12x128x128",
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      "ref": "akashsuper2000/panda-inference-w-36-tiles-256",
      "title": "PANDA Inference w/ 36 tiles_256",
      "source": "live"
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      "ref": "dararc/panda-step-1-tiling",
      "title": "PANDA: Step 1 Tiling",
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      "ref": "bluffmaster111/final",
      "title": "final",
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      "ref": "davejaivik/panda-bayesian",
      "title": "PANDA_Bayesian",
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      "ref": "razamh/panda-eda-better-visualization-simple-baseline",
      "title": "PANDA - EDA + Better Visualization+Simple Baseline",
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      "ref": "yjiaowla/panda-tiles-tf-keras-cohen-kappa-loss-baseline",
      "title": "PANDA: tiles, tf.keras, Cohen-kappa loss baseline",
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      "ref": "djeutsch/panda-deeper-eda-and-visualization-v2",
      "title": "PANDA-Deeper-EDA-and-Visualization-v2",
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      "ref": "tasnimnishatislam/dnmodelmanami-py",
      "title": "DNModelManami.py",
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      "ref": "hirune924/slideshow-viewer-for-the-lazy-person",
      "title": "Slideshow Viewer for the lazy person",
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      "ref": "tgibbons/cis-6115-unit-5-chap-10-11",
      "title": "CIS 6115 Unit 5 - Chap 10 & 11",
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      "ref": "hirune924/o2unet-loss-aggregate",
      "title": "O2UNet-loss-aggregate",
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      "ref": "naomiding/panda-concat-tile-pooling-n-16x128x128",
      "title": "PANDA concat tile pooling N=16x128x128",
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      "ref": "dararc/step-1a-pre-processing-resizing",
      "title": "Step 1A Pre Processing: Resizing",
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      "ref": "huynhdoo/panda-keras-model-inference",
      "title": "PANDA Keras model inference",
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      "ref": "duccongduong/25epoch-resnext50-tts-inference",
      "title": "25epoch_resnext50_tts_inference",
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      "ref": "congdd/kernel9786827f1f",
      "title": "kernel9786827f1f",
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      "ref": "harshris21/no-end-to-images-on-kaggle-eda-done-so-wrong",
      "title": "No End to Images on Kaggle: EDA done so wrong",
      "source": "live"
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    {
      "ref": "jahinalam/bbox-generation-for-prostate-cancer-and-images",
      "title": "bbox generation for prostate cancer 'AND' images",
      "source": "live"
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      "ref": "armenabnousi/yolo-obj-detection-4-prostate-cancer",
      "title": "yolo_obj_detection_4_prostate_cancer",
      "source": "live"
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      "ref": "tasnimnishatislam/very-fast-way-to-resize-image-panda",
      "title": "very fast way to resize image PANDA",
      "source": "live"
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      "ref": "nxrprime/panda-a-rather-simple-eda",
      "title": "PANDA: A rather simple EDA",
      "source": "live"
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      "ref": "santosh8896/panda-inference-w-36-tiles-256",
      "title": "PANDA Inference w/ 36 tiles_256",
      "source": "live"
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      "ref": "lvulliard/tune-pre-trained-efficient-b1",
      "title": "Tune pre-trained Efficient-B1",
      "source": "live"
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    {
      "ref": "gaur128/panda-tiles-on-tpu",
      "title": "PANDA Tiles on TPU",
      "source": "live"
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      "ref": "hirune924/image-loader-test",
      "title": "image loader test",
      "source": "live"
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      "ref": "iafoss/panda-init-class-128",
      "title": "Panda init class 128",
      "source": "live"
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    {
      "ref": "avirdee/fastai2-selective-mask-map-isup-grades",
      "title": "Fastai2 | Selective Mask| Map isup_grades ✔",
      "source": "live"
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    {
      "ref": "yukkyo/imagehash-to-detect-duplicate-images-and-grouping",
      "title": "Imagehash to detect duplicate images and grouping",
      "source": "live"
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      "ref": "lvulliard/test-panda-data",
      "title": "Test PANDA data",
      "source": "live"
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      "ref": "lvulliard/predict-panda-test-set",
      "title": "Predict PANDA test set",
      "source": "live"
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    {
      "ref": "appian/panda-imagehash-to-detect-duplicate-images",
      "title": "[PANDA] Imagehash to detect duplicate images",
      "source": "live"
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      "ref": "lvulliard/tune-pre-trained-xception",
      "title": "Tune pre-trained Xception",
      "source": "live"
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      "ref": "lvulliard/crop-images",
      "title": "Crop Images",
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    {
      "ref": "marsel171/lepsa",
      "title": "Lepsa",
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    {
      "ref": "nhan1212/stage-2-extract-radboud-from-a-given-data-h5",
      "title": "stage 2 (extract \"radboud\" from a given data.h5)",
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      "ref": "arkajyotib/lbp-cca-features-tune-upsample-better-image-tiles",
      "title": "LBP_CCA_features_tune_upsample_better_image_tiles",
      "source": "live"
    },
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      "ref": "rhtsingh/panda-36x256-tiles-albumentations-tf-records",
      "title": "PANDA - 36x256 Tiles, Albumentations & TF-Records ",
      "source": "live"
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      "ref": "huynhdoo/panda-wsi-tiles-preprocessing-script",
      "title": "PanDa WSI tiles preprocessing SCRIPT",
      "source": "live"
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      "ref": "haqishen/train-efficientnet-b0-w-36-tiles-256-lb0-87",
      "title": "Train EfficientNet-B0 w/ 36 tiles_256 [LB0.87]",
      "source": "live"
    },
    {
      "ref": "haqishen/panda-inference-w-36-tiles-256",
      "title": "PANDA Inference w/ 36 tiles_256",
      "source": "live"
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      "ref": "pmwaniki/finetune-resnet50",
      "title": "Finetune resnet50",
      "source": "live"
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    {
      "ref": "dararc/panda-step-1b-exploring-masks",
      "title": "Panda: Step 1B Exploring Masks",
      "source": "live"
    },
    {
      "ref": "huynhdoo/panda-wsi-tiles-preprocessing-eda",
      "title": "PanDa WSI tiles preprocessing EDA",
      "source": "live"
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    {
      "ref": "santosh8896/panda-eda-better-visualization-simple-baseline",
      "title": "PANDA - EDA + Better Visualization+Simple Baseline",
      "source": "live"
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      "ref": "rmicrobe/microanatomy-of-the-prostate",
      "title": "Microanatomy of the Prostate",
      "source": "live"
    },
    {
      "ref": "dararc/panda-step-1a-pre-processing-resizing",
      "title": "Panda: Step 1A Pre Processing Resizing",
      "source": "live"
    },
    {
      "ref": "fanconic/panda-tile-list-training-for-effnetb0-regression",
      "title": "PANDA tile-list training for EffNetB0 Regression",
      "source": "live"
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      "ref": "madfalcon/panda-keras-resnet50-submit",
      "title": "panda_keras_resnet50_submit",
      "source": "live"
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      "ref": "nobletp/panda-keras-baseline",
      "title": "PANDA keras baseline",
      "source": "live"
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      "ref": "norrsken/tiles",
      "title": "Tiles",
      "source": "live"
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    {
      "ref": "joydeb28/keras-with-pretrained-model",
      "title": "Keras with Pretrained Model ",
      "source": "live"
    },
    {
      "ref": "gaur128/panda-16x256x256-tiles",
      "title": "PANDA 16x256x256 tiles",
      "source": "live"
    },
    {
      "ref": "alexj21/panda-chowder-method",
      "title": "PANDA - CHOWDER method",
      "source": "live"
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    {
      "ref": "fanconic/panda-inference-for-effnetb0-regression",
      "title": "PANDA inference for EffNetB0 Regression",
      "source": "live"
    },
    {
      "ref": "thomasnikodem/mask-cleanup",
      "title": "mask cleanup",
      "source": "live"
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    {
      "ref": "ekami66/dynamic-vectorized-code-all-non-white-patches",
      "title": "[Dynamic vectorized code] All Non-white patches",
      "source": "live"
    },
    {
      "ref": "hannguyen/prostrate-cancer-detection",
      "title": "Prostrate Cancer Detection",
      "source": "live"
    },
    {
      "ref": "rnateghi/image-generation-1-staintools",
      "title": "Image Generation 1 (Staintools)",
      "source": "live"
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    {
      "ref": "raibektussupbekov/panda-crop-resize-to-square-save-train-data",
      "title": "PANDA: Crop, Resize to square & Save Train Data",
      "source": "live"
    },
    {
      "ref": "fanconic/panda-training-for-effnetb0-regression",
      "title": "PANDA training for EffNetB0 Regression",
      "source": "live"
    },
    {
      "ref": "nxrprime/panda-image-data-augmentation-techniques-w-monai",
      "title": "PANDA: Image Data Augmentation Techniques w/ MONAI",
      "source": "live"
    },
    {
      "ref": "mawanda/panda-save-space-with-sparse-tensors",
      "title": "PANDA: save space with sparse tensors",
      "source": "live"
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    {
      "ref": "alexj21/hybrid-cnn-lstm-starter",
      "title": "Hybrid CNN-LSTM : Starter",
      "source": "live"
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      "ref": "stasian/modification-of-iafoss-tiles",
      "title": "Modification of Iafoss tiles",
      "source": "live"
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    {
      "ref": "arroqc/panda-pytorchlightning-starter",
      "title": "PANDA PytorchLightning Starter",
      "source": "live"
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    {
      "ref": "rftexas/better-image-tiles-removing-white-spaces",
      "title": "Better image tiles - Removing white spaces",
      "source": "live"
    },
    {
      "ref": "dipetm/panda-remove-empty-space",
      "title": "PANDA Remove empty space",
      "source": "live"
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      "ref": "virajbagal/panda-seresnext50-regression-tpu",
      "title": "PANDA: SeResNext50 Regression TPU ",
      "source": "live"
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      "ref": "hengck23/kernel16867b0575",
      "title": "kernel16867b0575",
      "source": "live"
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    {
      "ref": "duccongduong/panda-challenge-part-3",
      "title": "PANDA Challenge Part 3",
      "source": "live"
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    {
      "ref": "duccongduong/pandas-challenge-part-2",
      "title": "PANDAS Challenge - Part 2",
      "source": "live"
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    {
      "ref": "sumitjha19/gleason-to-isup-score-resnet50",
      "title": "Gleason to ISUP  Score & Resnet50",
      "source": "live"
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    {
      "ref": "liorziv100/256x256-level-1-tiles",
      "title": "256x256 Level 1 tiles",
      "source": "live"
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    {
      "ref": "nhan1212/u-net-model-train-loss-eval-level-2-lowest",
      "title": "U-net_model(train_loss & eval),Level=2[lowest]",
      "source": "live"
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      "ref": "rftexas/implementing-gradcam-with-keras-for-error-analysis",
      "title": "Implementing GradCAM with Keras for error analysis",
      "source": "live"
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      "ref": "thieuma06/dataset-tiles-with-slides-of-overlay-mask",
      "title": "dataset: tiles with slides of overlay mask ",
      "source": "live"
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      "ref": "virajbagal/panda-se-resnext50-tpu-weights-inference",
      "title": "PANDA: Se_ResNext50 TPU Weights Inference  ",
      "source": "live"
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    {
      "ref": "akensert/panda-removal-of-pen-marks",
      "title": "[PANDA] Removal of pen marks",
      "source": "live"
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    {
      "ref": "hironobukawaguchi/panda-color-histograms-by-mask-label",
      "title": "PANDA color histograms by mask label",
      "source": "live"
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      "ref": "indranilbhattacharya/image-stats-submission-trial",
      "title": "Image stats - submission trial",
      "source": "live"
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      "ref": "virajbagal/tissue-detected-dataset",
      "title": "Tissue Detected Dataset",
      "source": "live"
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      "ref": "dannellyz/collection-of-600-suspicious-slides-data-loader",
      "title": "Collection of 600+ Suspicious Slides & Data Loader",
      "source": "live"
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      "ref": "norrsken/detailed-implementation-of-se-resnext50",
      "title": "Detailed implementation of SE_ResNeXt50",
      "source": "live"
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      "ref": "vgarshin/panda-keras-timedistributed",
      "title": "PANDA keras timedistributed",
      "source": "live"
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      "ref": "yida2311/panda-16x128x128-tiles",
      "title": "PANDA 16x128x128 tiles",
      "source": "live"
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    {
      "ref": "yuvaramsingh/panda-cnn-model-v1-tensorflow",
      "title": "PANDA_cnn_model_v1_tensorflow",
      "source": "live"
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      "ref": "roberthlee/reading-images-in-r",
      "title": "Reading images in R",
      "source": "live"
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      "ref": "nobletp/keras-vgg16-vgg19-inceptionv3-resnet50",
      "title": "Keras VGG16 -VGG19-InceptionV3- Resnet50",
      "source": "live"
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      "ref": "vgarshin/panda-keras-baseline",
      "title": "PANDA keras baseline",
      "source": "live"
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      "ref": "kst6690/kernel243f8be261",
      "title": "kernel243f8be261",
      "source": "live"
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    {
      "ref": "dararc/introduction-eda",
      "title": "Introduction & EDA",
      "source": "live"
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    {
      "ref": "gasparavit/panda-submission-test",
      "title": "PANDA submission test",
      "source": "live"
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    {
      "ref": "akashsuper2000/winitninference",
      "title": "winiTnInference",
      "source": "live"
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      "ref": "deejindal/panda-challenge-resnet-multitask-training",
      "title": "PANDA Challenge: ResNet multitask training",
      "source": "live"
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      "ref": "jakobw/minimal-submission-script",
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      "ref": "dgrechka/quadratic-weighted-kappa-for-tf-and-keras",
      "title": "Quadratic Weighted Kappa for TF (and Keras)",
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      "ref": "kaushal2896/seresnext50-labelsmoothing-training",
      "title": "SeResNext50 + LabelSmoothing: Training",
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      "ref": "dldmw579/reg-panda-cv-0-870",
      "title": "reg PANDA CV 0.870",
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      "ref": "xiejialun/panda-tiles-training-on-tensorflow-0-7-cv",
      "title": "PANDA tiles training on Tensorflow [0.7+ CV]",
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      "ref": "madfalcon/inceptionv3-keras-dummy-training-v1",
      "title": "inceptionV3 Keras dummy training_V1",
      "source": "live"
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    {
      "ref": "mudasserafzal/prostate-cancer",
      "title": "Prostate Cancer",
      "source": "live"
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    {
      "ref": "yasufuminakama/panda-se-resnext50-regression-baseline",
      "title": "PANDA / se_resnext50 regression baseline",
      "source": "live"
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      "ref": "dmitryhcl/tpu-effnet-b6-dataset-with-image-augmentation",
      "title": "tpu effnet b6 dataset with image augmentation",
      "source": "live"
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      "ref": "stillsut/my-tiling-3",
      "title": "my-tiling-3",
      "source": "live"
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      "ref": "foobar167/panda-dataset-getting-started",
      "title": "PANDA dataset - getting started",
      "source": "live"
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      "ref": "akashsuper2000/panda-challenge-densenet-on-tpus",
      "title": "PANDA Challenge: DenseNet On TPUs",
      "source": "live"
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      "ref": "stillsut/my-tiling-1",
      "title": "my-tiling-1",
      "source": "live"
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      "ref": "dannellyz/simple-tile-visualizer-and-subplot-modules",
      "title": "Simple Tile Visualizer and Subplot Modules",
      "source": "live"
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    {
      "ref": "raibektussupbekov/panda-eda",
      "title": "PANDA EDA",
      "source": "live"
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    {
      "ref": "debanga/let-s-enhance-the-images",
      "title": "Let's Enhance the Images!",
      "source": "live"
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    {
      "ref": "bootiu/eda-panda",
      "title": "EDA - PANDA",
      "source": "live"
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    {
      "ref": "harupy/how-to-embed-images-in-dataframe",
      "title": "How to embed images in dataframe",
      "source": "live"
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    {
      "ref": "harupy/visualization-panda-16x128x128-tiles",
      "title": "Visualization: PANDA 16x128x128 tiles",
      "source": "live"
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    {
      "ref": "norrsken/panda-prediction",
      "title": "PANDA Prediction",
      "source": "live"
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    {
      "ref": "prateekagnihotri/2-hrs-tpu-training-lb-0-68",
      "title": "2 hrs TPU training - LB(0.68)",
      "source": "live"
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      "ref": "shantanu1118/getting-started-with-the-panda-dataset",
      "title": "Getting started with the PANDA dataset",
      "source": "live"
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