Simple A3C implementation with pytorch + multiprocessing
关键参数与规格
关键参数与规格
参数
值
语言
Python
类别
Model Development
子类别
Architecture
关键词
Biomedical, Computer vision, Healthcare
星标数
661
分叉数
237
贡献者
1
创建日期
2017-12-26
更新日期
2026-10-11
1 天星标数
0
7 天星标数
1
排名
15,345
上次排名
15,160
排名变化
-185
7 天星标排名
11,005
上次 7 天星标排名
11,066
7 天星标排名变化
61
总星标排名
15,552
上次总星标排名
15,521
总星标排名变化
-31
国家
China
README 摘录
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. 4+ PyTorch and its dependencies Usage 1. Download images of ChestX-ray14 from this released page and decompress them to the directory images. Specify one or multiple GPUs and run 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. 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.
常见问题
记录了多少星标和分叉？
arnoweng/CheXNet 有 661 个星标和 237 个分叉。
仓库排名是多少？
arnoweng/CheXNet 排名第 15,345。
记录的语言和类别是什么？
arnoweng/CheXNet 有 661 个星标，语言为 Python，类别为 Model Development。
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.
Download images of ChestX-ray14 from this released page and decompress them to the directory images.
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).
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).
