This is a PyTorch implementation of the paper Zheng et al. Encoding histopathology WSIs with location-aware graphs for diagnostically relevant regions retrieval, Medical Image Analysis, 2022:
@Article{zheng2022encoding,
author = {Zheng, Yushan and Jiang, Zhiguo and Shi, Jun and Xie, Fengying and Zhang, Haopeng and
Luo, Wei and Hu, Dingyi and Sun, Shujiao and Jiang, Zhongmin and Xue, Chenghai},
title = {Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval},
journal = {Medical Image Analysis},
year = {2022},
volumn = {76},
pages = {102308},
doi = {https://doi.org/10.1016/j.media.2021.102308},
}
To train the LAGE-Net, please refer to run.sh:
The structure of the whole slide image dataset to run the code.
/media/dataset/endometrial # The directory of the data.
├─ 0A00DD22-A08E-4B47-A51B-94A8BD039DAA # The directory for a slide, which is named by GUID in our dataset.
│ ├─ Large # The directory of image tiles in Level 0 (40X lens).
│ │ ├─ 0000_0000.jpg # The image tile in Row 0 and Column 0.
│ │ ├─ 0000_0001.jpg # The image tile in Row 0 and Column 1.
│ │ └─ ...
│ ├─ Medium # The directory of image tiles in Level 1 (20X lens).
│ │ ├─ 0000_0000.jpg
│ │ ├─ 0000_0001.jpg
│ │ └─ ...
│ ├─ Small # The directory of image tiles in Level 2 (10X lens).
│ │ ├─ 0000_0000.jpg
│ │ ├─ 0000_0001.jpg
│ │ └─ ...
│ ├─ Overview # The directory of image tiles in Level 3 (5X lens).
│ │ ├─ 0000_0000.jpg
│ │ ├─ 0000_0001.jpg
│ │ └─ ...
│ ├─ Overview.jpg # The thumbnail of the WSI in Level 3.
│ └─ AnnotationMask.png # The pixel-wise annotation mask of the WSI in Level 3.
├─ 0A003711-3BE4-44E2-9280-89D84E5AF59F
└─ ...