lungXnet for Classification and Localization of Thoracic Diseases

#BRANCHES IN THE REPO

• master and summit - maintained by todd
• data_parallel and profiling - maintained by kshitij

DESCRIPTION

This is a Python3 (Pytorch) reimplementation of CheXNet. The model takes a chest X-ray image as input and outputs the probability of each thoracic disease along with a likelihood map of pathologies.

Dataset

The ChestX-ray14 dataset comprises 112,120 frontal-view chest X-ray images of 30,805 unique patients with 14 disease labels. To evaluate the model, we randomly split the dataset into training (70%), validation (10%) and test (20%) sets, following the work in paper. Partitioned image names and corresponding labels are placed under the directory labels.

Prerequisites

• Python 3.6+
• PyTorch and its dependencies

Usage

1. Clone this repository.

2. Download images of ChestX-ray14 from this released page and decompress them to the directory images.

3. Specify one or multiple GPUs and run

   python -m models.lungXnet --args

   Example:

   python -m models.lungXnet --data '/home/fa6/data/ChestXRay14/images' --trial --tag 'test'

Comparsion

We followed the training strategy described in the official paper, and a ten crop method is adopted both in validation and test. Compared with the original CheXNet, the per-class AUROC of our reproduced model is almost the same. We achieve a mean AUROC of 0.84

Pathology	Wang et al.	Yao et al.	CheXNet	lungeXnet
Atelectasis	0.716	0.772	0.8094	0.814
Cardiomegaly	0.807	0.904	0.9248	0.914
Effusion	0.784	0.859	0.8638	0.833
Infiltration	0.609	0.695	0.7345	0.710
Mass	0.706	0.792	0.8676	0.860
Nodule	0.671	0.717	0.7802	0.767
Pneumonia	0.633	0.713	0.7680	0.770
Pneumothorax	0.806	0.841	0.8887	0.870
Consolidation	0.708	0.788	0.7901	0.812
Edema	0.835	0.882	0.8878	0.897
Emphysema	0.815	0.829	0.9371	0.916
Fibrosis	0.769	0.767	0.8047	0.839
Pleural Thickening	0.708	0.765	0.8062	0.774
Hernia	0.767	0.914	0.9164	0.934

Contributions

This work was collaboratively conducted by Kshitij Srivastava, Folami Alamudun, Jackob Hinkle, Hongjun Yoon, and Georgia Tourassi.