This repo provides an exhaustive pipeline of processing TCGA Whole-Slide Images for downstream Multiple Instance Learning (MIL), basically developed from CLAM (https://github.com/mahmoodlab/CLAM).
Feel free to post your issue in this repo if you encounter any problems with this repo.
What this repo could help you in analyzing Whole-Slide Images with MIL is as follows:
- S01: Downloading slides from TCGA websites. It shows you, step by step, how to obtain the data you desire from TCGA.
- S02: Reorganizing slides at patient-level. It provides the code for organizing slides at patient-level, and show you how to get the useful label data (slide-level) that you would possibly utilize in downstream MIL tasks.
- S03: Segmenting and patching slides. It shows you the complete procedures and results of tissue segmentation and patching, based on an efficient preprocessing tool CLAM. Also, the basic knowledge of WSI structure is given.
- S04: Extracting patch features. It shows you the complete procedures and results of patch feature extraction, also based on the CLAM.
We thank CLAM's team [1] for contributing such an efficient and easy-to-use repo for WSI preprocessing, and TCGA [2] for making WSI data publicly-available to facilitate cancer research.
- [1] Lu, M. Y.; Williamson, D. F.; Chen, T. Y.; Chen, R. J.; Barbieri, M.; and Mahmood, F. 2021. Data-efficient and weakly supervised computational pathology on whole-slide images. Nature biomedical engineering, 5(6): 555–570.
- [2] Kandoth, C.; McLellan, M. D.; Vandin, F.; Ye, K.; Niu, B.; Lu, C.; Xie, M.; Zhang, Q.; McMichael, J. F.; Wycza- lkowski, M. A.; Leiserson, M. D. M.; Miller, C. A.; Welch, J. S.; Walter, M. J.; Wendl, M. C.; Ley, T. J.; Wilson, R. K.; Raphael, B. J.; and Ding, L. 2013. Mutational landscape and significance across 12 major cancer types. Nature, 502: 333–339.