ArticleNature communications2024
VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.International journal of molecular sciences · 2026Review
- sCellST predicts single-cell gene expression from H& E images.Nature communications · 2026Article
- Contrastive learning enhances fairness in pathology artificial intelligence systems.Cell reports. Medicine · 2025Article
- HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.Patterns (New York, N.Y.) · 2025Article
- Automated cell annotation and classification on histopathology for spatial biomarker discovery.Nature communications · 2025Article
- Remodeling the tumor microenvironment: regulatory effects of β-sitosterol and luteolin on the immunosuppressive milieu in endometrial carcinoma and implications for combinatorial immunotherapy.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
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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Registered trials
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