Paper: Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks

Patch TCGA in 200μm (PTCGA200)

A imagenet-1k equivalent (~5M) dataset of pathological hematoxylin and eosin (H&E) images. Patches are cropped in 512x512px (200x200μm), or in 0.39 microns per pixel from the tissue region. Refer the original paper for the details. Use the snippet below to make the original archive file from divided files.Make sure the md5sum is concordant with PTCGA200_md5.txt.

$ cat PTCGA200_p_* > PTCGA200.tar.gz

Please cite below for this dataset. ## Citation If you use this work, datasets, and models, please cite the following paper: bibtex @InProceedings{10.1007/978-3-031-44917-8_25, author = {Kawai, Masakata and Ota, Noriaki and Yamaoka, Shinsuke}, editor = {Xue, Zhiyun and Antani, Sameer and Zamzmi, Ghada and Yang, Feng and Rajaraman, Sivaramakrishnan and Huang, Sharon Xiaolei and Linguraru, Marius George and Liang, Zhaohui}, title = {Large-Scale Pretraining on Pathological Images for Fine-Tuning of Small Pathological Benchmarks}, booktitle = {Medical Image Learning with Limited and Noisy Data}, year = {2023}, publisher = {Springer Nature Switzerland}, address = {Cham}, pages = {257--267}, isbn = {978-3-031-44917-8} }

license: other license_name: nih-gds-policy license_link: https://datasharing.cancer.gov/post/Guidance/genomic-data-sharing/