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.

本研究针对组织病理图像中的少样本分类问题展开，填补了该领域应用不足的空白。研究评估了多种最先进的少样本学习方法在不同场景下的表现，发现最佳方法在不同的少样本场景下达到了70%以上、80%以上和85%以上的准确率，表明了针对专业领域如组织病理图像的计算机视觉技术的显著进展。

少样本组织病理图像分类：评估最先进的方法并揭示性能洞察