A pytorch reimplementation of CheXNet
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README.md

CheXNet for Classification and Localization of Thoracic Diseases

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 3.4+
  • 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 model.py

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 have also proposed a slightly-improved model which achieves a mean AUROC of 0.847 (v.s. 0.841 of the original CheXNet).

Pathology Wang et al. Yao et al. CheXNet Our Implemented CheXNet Our Improved Model
Atelectasis 0.716 0.772 0.8094 0.8294 0.8311
Cardiomegaly 0.807 0.904 0.9248 0.9165 0.9220
Effusion 0.784 0.859 0.8638 0.8870 0.8891
Infiltration 0.609 0.695 0.7345 0.7143 0.7146
Mass 0.706 0.792 0.8676 0.8597 0.8627
Nodule 0.671 0.717 0.7802 0.7873 0.7883
Pneumonia 0.633 0.713 0.7680 0.7745 0.7820
Pneumothorax 0.806 0.841 0.8887 0.8726 0.8844
Consolidation 0.708 0.788 0.7901 0.8142 0.8148
Edema 0.835 0.882 0.8878 0.8932 0.8992
Emphysema 0.815 0.829 0.9371 0.9254 0.9343
Fibrosis 0.769 0.767 0.8047 0.8304 0.8385
Pleural Thickening 0.708 0.765 0.8062 0.7831 0.7914
Hernia 0.767 0.914 0.9164 0.9104 0.9206

Contributions

This work was collaboratively conducted by Xinyu Weng, Nan Zhuang, Jingjing Tian and Yingcheng Liu.

Our Team

All of us are students/interns of Machine Intelligence Lab, Institute of Computer Science & Technology, Peking University, directed by Prof. Yadong Mu (http://www.muyadong.com).