NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level
  Multi-Class Classification in Whole-Slide Images
Gia-Bao Le Van-Tien Nguyen Trung-Nghia Le Minh-Triet Tran
Whole-slide image (WSI) analysis plays a crucial role in cancer diagnosis and
treatment. In addressing the demands of this critical task, self-supervised
learning (SSL) methods have emerged as a valuable resource, leveraging their
efficiency in circumventing the need for a large number of annotations, which
can be both costly and time-consuming to deploy supervised methods.
Nevertheless, patch-wise representation may exhibit instability in performance,
primarily due to class imbalances stemming from patch selection within WSIs. In
this paper, we introduce Nearby Patch Contrastive Learning (NearbyPatchCL), a
novel self-supervised learning method that leverages nearby patches as positive
samples and a decoupled contrastive loss for robust representation learning.
Our method demonstrates a tangible enhancement in performance for downstream
tasks involving patch-level multi-class classification. Additionally, we curate
a new dataset derived from WSIs sourced from the Canine Cutaneous Cancer
Histology, thus establishing a benchmark for the rigorous evaluation of
patch-level multi-class classification methodologies. Intensive experiments
show that our method significantly outperforms the supervised baseline and
state-of-the-art SSL methods with top-1 classification accuracy of 87.56%. Our
method also achieves comparable results while utilizing a mere 1% of labeled
data, a stark contrast to the 100% labeled data requirement of other
approaches. Source code: https://github.com/nvtien457/NearbyPatchCL