Simple A3C implementation with pytorch + multiprocessing.
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Model Development
Subcategory
Architecture
Keywords
Biomedical, Computer vision, Healthcare
Stars
661
Forks
237
Contributors
1
Created date
2017-12-26
Updated date
2026-10-11
Stars in 1 day
0
Stars in 7 days
1
Rank
15,345
Previous rank
15,160
Rank change
-185
7-day stars rank
11,005
Previous 7-day stars rank
11,066
7-day stars rank change
61
Total stars rank
15,552
Previous total stars rank
15,521
Total stars rank change
-31
Country
China
README summary
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.
Frequently asked questions
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arnoweng/CheXNet has 661 stars and 237 forks.
What is the repository rank?
arnoweng/CheXNet is ranked 15,345.
What language and category are recorded?
arnoweng/CheXNet has 661 stars, uses Python and belongs to Model Development.
Which repository is closest in rank?
arnoweng/CheXNet is compared with appliedAI-Initiative-old/orb_slam_2_ros, ranked 15,344 with 661 stars.
README
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).
