# Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning

**Authors:** Jiawei Yang, Hanbo Chen, Jiangpeng Yan, Xiaoyu Chen, Jianhua Yao  
**arXiv:** [2202.09059](https://arxiv.org/abs/2202.09059)  
**Date:** 2022-02-18  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2202.09059v1/

## Key points

- Few-shot learning has been well-established in natural images but has seen limited application in histology images.
- A study was conducted to explore few-shot learning in histology images by designing cross-domain tasks mimicking real-world clinical scenarios.
- The study proposed integrating contrastive learning (CL) and latent augmentation (LA) to develop a few-shot system for label-efficient learning and improved generalizability in histology image analysis.
- CL enables learning useful representations without manual labels, while LA facilitates the transfer of semantic variations from the base dataset in an unsupervised manner.
- Models trained using CL showed superior generalization capabilities compared to traditional supervised learning methods when applied to unseen classes within histology images.
- Incorporating LA consistently led to performance improvements over baseline approaches.
- Histology images naturally lend themselves to studies on self-supervised learning due to their complex visual content.

## Summary

In recent years, few-shot learning has emerged as a well-established topic in the field of natural images. However, there has been limited focus on applying this concept to histology images, which hold significant clinical value due to the challenges associated with collecting well-labeled datasets and rare abnormal samples. To address this gap, a study was conducted to explore few-shot learning in histology images by designing three cross-domain tasks that mimic real-world clinical scenarios. To enhance label-efficient learning and improve generalizability in histology image analysis, the study proposed integrating contrastive learning (CL) with latent augmentation (LA) to develop a few-shot system. CL enables the learning of useful representations without manual labels, while LA facilitates the transfer of semantic variations from the base dataset in an unsupervised manner. By leveraging these two components, the model can effectively utilize unlabeled training data and adapt to other label-intensive problems seamlessly. Experimental results revealed that models trained using CL exhibited superior generalization capabilities compared to those trained using traditional supervised learning methods when applied to unseen classes within histology images. Additionally, incorporating LA consistently led to performance improvements over baseline approaches. While previous research on self-supervised learning primarily focused on ImageNet-like images featuring dominant objects at their centers, recent attention has shifted towards images with multiple objects and textures. Histology images naturally lend themselves to such studies due to their complex visual content. The findings of this study not only demonstrate the effectiveness of CL for improving generalization in histology image analysis but also contribute valuable insights into how models generalize in terms of both representation learning and histological image interpretation. The availability of code further enhances reproducibility and encourages future research efforts in this domain. Authors Jiawei Yang, Hanbo Chen, Jiangpeng Yan, Xiaoyu Chen, and Jianhua Yao have made a significant contribution towards advancing our understanding of few-shot classification in histology images through contrastive learning techniques.