jnb131/chest-pathology-classification-using-a-multimodal-view

A View-Specific Approach to Chest Pathology Classification with Uncertainty Labels

This was adapted from zoogzog/chexpert

The highest accuracy evaluated with AUROC was 0.847 Training (70%), validation (20%) and testing (10%) datasets were used in this implementation.

Prerequisites

• Python 3.5.2
• Pytorch
• OpenCV (for generating CAMs)

Usage

• Download the ChestX-ray14 database from here
• Unpack archives in separate directories (e.g. images_001.tar.gz into images_001)
• Run python Main.py to run test using the pre-trained model (m-25012018-123527)
• Use the runTrain() function in the Main.py to train a model from scratch

This implementation allows to conduct experiments with 3 different densenet architectures: densenet-121, densenet-169 and densenet-201.

• To generate CAM of a test file run script HeatmapGenerator

Results

The highest accuracy 0.8508 was achieved by the model m-25012018-123527 (see the models directory).

Pathology	AUROC
Atelectasis	0.8321
Cardiomegaly	0.9107
Effusion	0.8860
Infiltration	0.7145
Mass	0.8653
Nodule	0.8037
Pneumonia	0.7655
Pneumothorax	0.8857
Consolidation	0.8157
Edema	0.9017
Emphysema	0.9422
Fibrosis	0.8523
P.T.	0.7948
Hernia	0.9416

Computation time

The training was done using single Tesla P100 GPU and took approximately 22h.