NIH-Chest-X-rays-Multi-Label-Image-Classification-In-Pytorch

Multi-Label Image Classification of the Chest X-Rays In Pytorch

Requirements

• torch >= 0.4
• torchvision >= 0.2.2
• opencv-python
• numpy >= 1.7.3
• matplotlib
• tqdm

Dataset

NIH Chest X-ray Dataset is used for Multi-Label Disease Classification of of the Chest X-Rays.
There are a total of 15 classes (14 diseases, and one for ‘No findings’)
Images can be classified as “No findings” or one or more disease classes:

• Atelectasis
• Consolidation
• Infiltration
• Pneumothorax
• Edema
• Emphysema
• Fibrosis
• Effusion
• Pneumonia
• Pleural_thickening
• Cardiomegaly
• Nodule Mass
• Hernia

There are 112,120 X-ray images of size 1024x1024 pixels, in which 86,524 images are for training and 25,596 are for testing.

Sample X-Ray Images

Model

Pretrained Resnet50 model is used for Transfer Learning on this new image dataset.

Loss Function

There is a choice of loss function

• Focal Loss (default)
• Binary Cross Entropy Loss or BCE Loss

Training

• From Scratch

  Following are the layers which are set to trainable-

  • layer2
  • layer3
  • layer4

  • fc

    Terminal Code:

    python main.py

• Resuming From a Saved Checkpoint

  A Saved Checkpoint needs to be loaded which is nothing but a dictionary containing the

  • epochs (number of epochs the model has been trained till that time)
  • model (architecture and the learnt weights of the model)
  • lr_scheduler_state_dict (state_dict of the lr_scheduler)

  • losses_dict (a dictionary containing the following loses)

    • mean train epoch losses for all the epochs
    • mean val epoch losses for all the epochs
    • batch train loss for all the training batches
    • batch train loss for all the val batches

Different layers of the model are freezed/unfreezed in different stages, defined at the end of *this README.md file, to fit the model well on the data. The ‘stage’ parameter can be passed from the terminal using the argument —stage STAGE

Terminal Code:

python main.py --resume --ckpt checkpoint_file.pth --stage 2

Training the model will create a models directory and will save the checkpoints in there.

Testing

A Saved Checkpoint needs to be loaded using the —ckpt argument and —test argument needs to be passed for activating the Test Mode

Terminal Code:

python main.py --test --ckpt checkpoint_file.pth

Result

The model achieved the average ROC AUC Score of 0.73241 on all classes(excluding “No findings” class) after training in the following stages-

STAGE 1

• Loss Function: FocalLoss
• lr: 1e-5
• Training Layers: layer2, layer3, layer4, fc
• Epochs: 2

STAGE 2

• Loss Function: FocalLoss
• lr: 3e-4
• Training Layers: layer3, layer4, fc
• Epochs: 1

STAGE 3

• Loss Function: FocalLoss
• lr: 1e-3
• Training Layers: layer4, fc
• Epochs: 3

STAGE 4

• Loss Function: FocalLoss
• lr: 1e-3
• Training Layers: fc
• Epochs: 2