CCRL: Contrastive Cell Representation Learning

TL;DR

This paper introduces CCRL, a contrastive self-supervised learning model for cell clustering in histopathology images, outperforming existing methods and reducing the need for annotated data, especially effective with fewer cell categories.

Contribution

The study proposes CCRL, a novel contrastive self-supervised learning approach for cell clustering, demonstrating superior performance and efficiency over existing models across different tissue datasets.

Findings

CCRL outperforms existing cell clustering models significantly.

The model works well with few cell categories, unlike traditional SSL models.

Eliminates the need for extensive data annotation, enabling larger dataset training.

Abstract

Cell identification within the H&E slides is an essential prerequisite that can pave the way towards further pathology analyses including tissue classification, cancer grading, and phenotype prediction. However, performing such a task using deep learning techniques requires a large cell-level annotated dataset. Although previous studies have investigated the performance of contrastive self-supervised methods in tissue classification, the utility of this class of algorithms in cell identification and clustering is still unknown. In this work, we investigated the utility of Self-Supervised Learning (SSL) in cell clustering by proposing the Contrastive Cell Representation Learning (CCRL) model. Through comprehensive comparisons, we show that this model can outperform all currently available cell clustering models by a large margin across two datasets from different tissue types. More interestingly, the results show that our proposed model worked well with a few number of cell categories while the utility of SSL models has been mainly shown in the context of natural image datasets with large numbers of classes (e.g., ImageNet). The unsupervised representation learning approach proposed in this research eliminates the time-consuming step of data annotation in cell classification tasks, which enables us to train our model on a much larger dataset compared to previous methods. Therefore, considering the promising outcome, this approach can open a new avenue to automatic cell representation learning.