This paper presents a new approach for classifying 2D histopathology patches using few-shot learning. The method is designed to tackle a significant challenge in histopathology, which is the limited availability of labeled data. By applying a sliding window technique to histopathology slides, we illustrate the practical benefits of transductive learning (i.e., making joint predictions on patches) to achieve consistent and accurate classification. Our approach involves an optimization-based strategy that actively penalizes the prediction of a large number of distinct classes within each window. We conducted experiments on histopathological data to classify tissue classes in digital slides of liver cancer, specifically hepatocellular carcinoma. The initial results show the effectiveness of our method and its potential to enhance the process of automated cancer diagnosis and treatment, all while reducing the time and effort required for expert annotation.

翻译：本文提出了一种基于少样本学习的二维组织病理图像块分类新方法。该方法旨在解决组织病理学中标注数据稀缺这一重大挑战。通过将滑动窗口技术应用于组织病理切片，我们展示了直推式学习（即对图像块进行联合预测）在实现一致且准确分类方面的实际优势。我们的方法采用基于优化的策略，主动惩罚每个窗口内预测出大量不同类别的行为。我们在组织病理数据上开展了实验，对肝癌（特别是肝细胞癌）数字切片中的组织类别进行分类。初步结果表明了该方法的有效性及其在提升自动化癌症诊断与治疗流程方面的潜力，同时能够减少专家标注所需的时间和精力。