Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

Abstract

Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the performance of different baseline state-of-the-art (SOTA) DL models in classifying ALN metastatic status based on CNB images. An extensive ablation study of various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed.

arXiv commentAccepted for MICCAI DEMI Workshop 2023

References in corpus (4)

• Deep Residual Learning for Image Recognition
• AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
• Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
• RandAugment: Practical automated data augmentation with a reduced search space