Zero-Shot Whole Slide Image Retrieval in Histopathology Using Embeddings of Foundation Models
<|reference_start|>Zero-Shot Whole Slide Image Retrieval in Histopathology Using Embeddings of Foundation Models: We have tested recently published foundation models for histopathology for image retrieval. We report macro average of F1 score for top-1 retrieval, majority of top-3 retrievals, and majority of top-5 retrievals. We perform zero-shot retrievals, i.e., we do not alter embeddings and we do not train any classifier. As test data, we used diagnostic slides of TCGA, The Cancer Genome Atlas, consisting of 23 organs and 117 cancer subtypes. As a search platform we used Yottixel that enabled us to perform WSI search using patches. Achieved F1 scores show low performance, e.g., for top-5 retrievals, 27% +/- 13% (Yottixel-DenseNet), 42% +/- 14% (Yottixel-UNI), 40%+/-13% (Yottixel-Virchow), 41%+/-13% (Yottixel-GigaPath), and 41%+/-14% (GigaPath WSI).<|reference_end|>
@article{alfasly2024zero-shot,
    title={Zero-Shot Whole Slide Image Retrieval in Histopathology Using Embeddings
  of Foundation Models},
    author={Saghir Alfasly, Ghazal Alabtah, Sobhan Hemati, Krishna Rani Kalari,
  H.R. Tizhoosh},
    journal={arXiv preprint arXiv:2409.04631},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04631},
    primaryClass={eess.IV cs.AI cs.CV}
}