一种用于肝癌数字组织病理切片分类的转导式小样本学习方法
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer
AI总结：
针对组织病理标注数据不足的问题，提出基于转导学习的小样本分类方法，通过滑动窗口联合预测与类别数惩罚优化，在肝癌切片分类中取得有效结果，可降低标注成本并辅助癌症诊疗。
摘要：
本文提出了一种利用小样本学习对二维组织病理学图像块进行分类的新方法。该方法旨在解决组织病理学领域标注数据有限这一重大挑战。通过将滑动窗口技术应用于组织病理切片，我们阐明了转导学习（即对图像块进行联合预测）在实现稳定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情况。我们在组织病理学数据上开展了实验，对肝癌（具体为肝细胞癌）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，以及其在提升自动化癌症诊断与治疗流程、同时减少专家标注所需时间与精力方面的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
