A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer

arXiv:2311.17740v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv availability cancer challenge classification cs.lg data digital eess.iv few-shot few-shot learning paper q-bio.to slides type