Histopathology Slide Indexing and Search: Are We There Yet?

The search and retrieval of digital histopathology slides is an important task that has yet to be solved. In this case study, we investigate the clinical readiness of three state-of-the-art histopathology slide search engines, Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a qualitative assessment of each model's performance in providing retrieval results that are reliable and useful to pathologists. We found that all three image search engines fail to produce consistently reliable results and have difficulties in capturing granular and subtle features of malignancy, limiting their diagnostic accuracy. Based on our findings, we also propose a minimal set of requirements to further advance the development of accurate and reliable histopathology image search engines for successful clinical adoption.

The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection

Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight.

Multimodal Pathology Image Search Between H&E Slides and Multiplexed Immunofluorescent Images

We present an approach for multimodal pathology image search, using dynamic time warping (DTW) on Variational Autoencoder (VAE) latent space that is fed into a ranked choice voting scheme to retrieve multiplexed immunofluorescent imaging (mIF) that is most similar to a query H&E slide. Through training the VAE and applying DTW, we align and compare mIF and H&E slides. Our method improves differential diagnosis and therapeutic decisions by integrating morphological H&E data with immunophenotyping from mIF, providing clinicians a rich perspective of disease states. This facilitates an understanding of the spatial relationships in tissue samples and could revolutionize the diagnostic process, enhancing precision and enabling personalized therapy selection. Our technique demonstrates feasibility using colorectal cancer and healthy tonsil samples. An exhaustive ablation study was conducted on a search engine designed to explore the correlation between multiplexed Immunofluorescence (mIF) and Hematoxylin and Eosin (H&E) staining, in order to validate its ability to map these distinct modalities into a unified vector space. Despite extreme class imbalance, the system demonstrated robustness and utility by returning similar results across various data features, which suggests potential for future use in multimodal histopathology data analysis.