# Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights

arXiv: https://arxiv.org/abs/2408.13816 · HTML: https://mlsanity.com/paper/2408.13816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning](https://mlsanity.com/paper/1911.04623/) (2019)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [Laplacian Regularized Few-Shot Learning](https://mlsanity.com/paper/2006.15486/) (2020)
- [Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images](https://mlsanity.com/paper/2006.13276/) (2020)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2103.10178/) (2021)
- [Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning](https://mlsanity.com/paper/2202.09059/) (2022)

## Built on by (most cited first, 1 of 1)

- [Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images](https://mlsanity.com/paper/2410.09176/) (2024)

## Abstract (arXiv)

This paper presents a study on few-shot classification in the context of histopathology images. While few-shot learning has been studied for natural image classification, its application to histopathology is relatively unexplored. Given the scarcity of labeled data in medical imaging and the inherent challenges posed by diverse tissue types and data preparation techniques, this research evaluates the performance of state-of-the-art few-shot learning methods for various scenarios on histology data. We have considered four histopathology datasets for few-shot histopathology image classification and have evaluated 5-way 1-shot, 5-way 5-shot and 5-way 10-shot scenarios with a set of state-of-the-art classification techniques. The best methods have surpassed an accuracy of 70%, 80% and 85% in the cases of 5-way 1-shot, 5-way 5-shot and 5-way 10-shot cases, respectively. We found that for histology images popular meta-learning approaches is at par with standard fine-tuning and regularization methods. Our experiments underscore the challenges of working with images from different domains and underscore the significance of unbiased and focused evaluations in advancing computer vision techniques for specialized domains, such as histology images.