组织病理学
代表(政治)
计算机科学
人工智能
自然语言处理
病理
医学
政治学
政治
法学
作者
Ramin Nakhli,Katherine Rich,Allen Zhang,Amirali Darbandsari,Elahe Shenasa,Amir Hadjifaradji,Sidney Thiessen,Katy Milne,Steve Jones,Jessica N. McAlpine,Brad H. Nelson,C. Blake Gilks,Hossein Farahani,Ali Bashashati
标识
DOI:10.1038/s41467-024-48062-1
摘要
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.
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