Core Concepts
The author presents a transductive few-shot learning approach to classify histopathological images, addressing challenges of limited data availability and class imbalance.
Abstract
The paper introduces a novel method for classifying histopathology patches using few-shot learning to overcome data scarcity and imbalanced classes. By applying transductive learning on liver cancer slides, the approach shows promise in automating cancer diagnosis efficiently. The study highlights the benefits of joint predictions on localized regions to enhance classification accuracy and reliability. The method involves an optimization-based strategy that minimizes the prediction of multiple classes within each window, demonstrating practical benefits in automated cancer diagnosis and treatment.
Stats
Class distribution: NT (26%), RE (14%), AM (8%), VE (12%), AN (40%)
Penalty parameter λ set to 1250 for validation slides
Downsampled mini-patches to 512x512 resolution from 1728x1728 patches
Support set comprised annotated patches from 28 patients
Quotes
"In clinical settings, histopathology images are a critical primary source of information for pathologists."
"Few-shot learning methods address the limitations found in traditional supervised learning techniques."
"Our method surpasses other approaches, highlighting the benefits of using an appropriate Gaussian metric and transductive inference."