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.

本文提出了一种新的方法，利用少样本学习对 2D 组织学病理学图像进行分类。通过在组织学切片上应用滑动窗口技术，我们揭示了转导学习（即在补丁上进行联合预测）在实现一致准确分类中的实际效益。我们的方法采用基于优化的策略，主动惩罚每个窗口内大量不同类别的预测。我们在组织学数据上进行实验证明了我们方法的有效性以及提高自动癌症诊断和治疗过程的潜力，同时减少了专家标注所需的时间和精力。