# VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology

arXiv: https://arxiv.org/abs/2303.04696 · HTML: https://mlsanity.com/paper/2303.04696/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 69 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [A Simple Semi-Supervised Learning Framework for Object Detection](https://mlsanity.com/paper/2005.04757/) (2020)
- [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [LambdaNetworks: Modeling Long-Range Interactions Without Attention](https://mlsanity.com/paper/2102.08602/) (2021)
- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [Mitosis domain generalization in histopathology images -- The MIDOG challenge](https://mlsanity.com/paper/2204.03742/) (2022)
- [CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1909.01068/) (2019)
- [On Mutual Information in Contrastive Learning for Visual Representations](https://mlsanity.com/paper/2005.13149/) (2020)
- [Automatic Shortcut Removal for Self-Supervised Representation Learning](https://mlsanity.com/paper/2002.08822/) (2020)
- [NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation](https://mlsanity.com/paper/2102.09099/) (2021)
- [Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks](https://mlsanity.com/paper/1711.11317/) (2017)
- [A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning](https://mlsanity.com/paper/2111.05882/) (2021)
- [CCRL: Contrastive Cell Representation Learning](https://mlsanity.com/paper/2208.06445/) (2022)

## Built on by (most cited first, 6 of 6)

- [Graph Neural Networks in Histopathology: Emerging Trends and Future Directions](https://mlsanity.com/paper/2406.12808/) (2024)
- [Divide and Conquer Self-Supervised Learning for High-Content Imaging](https://mlsanity.com/paper/2503.07444/) (2025)
- [Adaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images](https://mlsanity.com/paper/2511.13586/) (2025)
- [Context-aware Skin Cancer Epithelial Cell Classification with Scalable Graph Transformers](https://mlsanity.com/paper/2602.15783/) (2026)
- [MAD: Microenvironment-Aware Distillation -- A Pretraining Strategy for Virtual Spatial Omics from Microscopy](https://mlsanity.com/paper/2603.13401/) (2026)
- [LEMON: a foundation model for nuclear morphology in Computational Pathology](https://mlsanity.com/paper/2603.25802/) (2026)

## Abstract (arXiv)

In clinical practice, many diagnosis tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques require labels, providing manual cell annotations is time-consuming due to the large number of cells. In this paper, we propose a self-supervised framework (VOLTA) for cell representation learning in histopathology images using a novel technique that accounts for the cell's mutual relationship with its environment for improved cell representations. We subjected our model to extensive experiments on the data collected from multiple institutions around the world comprising of over 700,000 cells, four cancer types, and cell types ranging from three to six categories for each dataset. The results show that our model outperforms the state-of-the-art models in cell representation learning. To showcase the potential power of our proposed framework, we applied VOLTA to ovarian and endometrial cancers with very small sample sizes (10-20 samples) and demonstrated that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide novel insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised deep learning models that require large sample sizes for training, we provide a framework that can empower new discoveries without any annotation data in situations where sample sizes are limited.