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PyTorch implementation of CheXNet: Radiologist level pneumonia detection using deep learning

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CheXNet

This is part of my internship at Endimension Technology, IIT Bombay.

PyTorch implementation of CheXNet: Radiologist level pneumonia detection using deep learning based on this implementation.

You can run the complete notebook on Kaggle -> CheXNet-PyTorch

Hyperparameters

Batch size Learning Rate Epochs Time
64 0.01 20 2 hrs

Per Class AUROC

Pathology AUROC
Atelectasis 0.735
Cardiomegaly 0.882
Effusion 0.82
Infiltration 0.673
Mass 0.788
Nodule 0.728
Pneumonia 0.647
Pneumothorax 0.799
Consolidation 0.689
Edema 0.832
Emphysema 0.858
Fibrosis 0.77
Pleural_Thickening 0.719
Hernia 0.846

You can find the internship certificate here

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PyTorch implementation of CheXNet: Radiologist level pneumonia detection using deep learning

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