  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.
