VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology
This addresses the challenge of limited annotations in histopathology for clinical diagnosis, offering a method for new discoveries without large labeled datasets, though it is incremental as it builds on self-supervised learning.
The authors tackled the problem of cell representation learning in histopathology images by proposing VOLTA, a self-supervised framework that accounts for cell-environment relationships, which outperformed state-of-the-art models on datasets with over 700,000 cells and enabled insights into ovarian and endometrial cancers with small sample sizes (10-20 samples).
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.