  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.
