Few-Shot Lymph Node Metastasis Classification Meets High Performance on Whole Slide Images via the Informative Non-Parametric Classifier

Yi Li, Qixiang ZHANG, Tianqi Xiang, Yiqun Lin, Qingling Zhang, Xiaomeng Li

Abstract: Lymph node metastasis (LNM) classification is crucial for breast cancer staging. However, the process of identifying tiny metastatic cancer cells within gigapixel whole slide image (WSI) is tedious, time-consuming, and expensive. To address this challenge, computational pathology methods have emerged, particularly multiple instance learning (MIL) based on deep learning. But these methods require massive amounts of data, while existing few-shot methods severely compromise accuracy for data saving. To simultaneously achieve few-shot and high performance LNM classification, we propose the informative non-parametric classifier (INC). It maintains informative local patch features divided by mask label, then innovatively utilizes non-parametric similarity to classify LNM, avoiding overfitting on a few WSI examples. Experimental results demonstrate that the proposed INC outperforms existing SoTA methods across various settings, with less data and labeling cost. For the same setting, we achieve remarkable AUC improvements over 36.76% on CAMELYON16. Additionally, our approach demonstrates excellent generalizability across multiple medical centers and corrupted WSIs, even surpassing many-shot SoTA methods over 7.55% on CAMELYON16-C. Code is available at https://github.com/xmed-lab/INC.