Zero-Shot Whole Slide Image Retrieval in Histopathology Using Embeddings of Foundation Models

TL;DR

This study evaluates the effectiveness of foundation models for zero-shot whole slide image retrieval in histopathology, revealing limited performance across diverse cancer types and organs.

Contribution

It provides a comprehensive assessment of foundation models' zero-shot capabilities for histopathology image retrieval without additional training.

Findings

Low F1 scores across models and retrieval levels.

Performance varies significantly between models.

Zero-shot approach shows limited effectiveness.

Abstract

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).