Python-CheXNet的Python3Pytorch重新实现

# CheXNet for Classification and Localization of Thoracic Diseases This is a Python3 (Pytorch) reimplementation of [CheXNet](https://stanfordmlgroup.github.io/projects/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. <div align=center><img width="500" height="500" src="./localization/00008473_011-3.png"/></div> ## Dataset The [ChestX-ray14 dataset](http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf) 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](./ChestX-ray14/labels). ## Prerequisites - Python 3.4+ - [PyTorch](http://pytorch.org/) and its dependencies ## Usage 1. Clone this repository. 2. Download images of ChestX-ray14 from this [released page](https://nihcc.app.box.com/v/ChestXray-NIHCC) and decompress them to the directory [images](./ChestX-ray14/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.](https://arxiv.org/abs/1705.02315) | [Yao et al.](https://arxiv.org/abs/1710.10501) | [CheXNet](https://arxiv.org/abs/1711.05225) | 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).