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Conference Papers Year : 2024

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

Une approche d'apprentissage few-shot transductive pour la classification de lames histopathologiques numériques du cancer du foie

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 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.
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Dates and versions

hal-04305713 , version 1 (24-11-2023)
hal-04305713 , version 2 (09-03-2024)

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Aymen Sadraoui, Ségolène Martin, Eliott Barbot, Astrid Laurent-Bellue, Jean-Christophe Pesquet, et al.. A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer. ISBI 2024 - 21st IEEE International Symposium on Biomedical Imaging, May 2024, Athènes, Greece. ⟨hal-04305713v2⟩
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