VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology

Ramin Nakhli, Katherine Rich, Allen Zhang, Amirali Darbandsari, Elahe Shenasa, Amir Hadjifaradji, Sidney Thiessen, Katy Milne, Steven J. M. Jones, Jessica N. McAlpine, Brad H. Nelson, C. Blake Gilks, Hossein Farahani and Ali Bashashati ()
Additional contact information
Ramin Nakhli: University of British Columbia
Katherine Rich: University of British Columbia
Allen Zhang: University of British Columbia
Amirali Darbandsari: University of British Columbia
Elahe Shenasa: University of British Columbia
Amir Hadjifaradji: University of British Columbia
Sidney Thiessen: BC Cancer Agency
Katy Milne: BC Cancer Agency
Steven J. M. Jones: BC Cancer Research Institute
Jessica N. McAlpine: University of British Columbia
Brad H. Nelson: BC Cancer Agency
C. Blake Gilks: University of British Columbia
Hossein Farahani: University of British Columbia
Ali Bashashati: University of British Columbia

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell’s mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.

Date: 2024
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DOI: 10.1038/s41467-024-48062-1

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