Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

TL;DR

This study introduces a streamlined deep learning pipeline for predicting axillary lymph node metastasis from digital biopsy images, demonstrating competitive performance with reduced steps and evaluating the impact of data augmentation and manual annotations.

Contribution

The paper presents a simplified whole slide multiple instance learning approach for metastasis prediction, reducing the number of steps compared to existing methods.

Findings

Deep learning models achieved high accuracy in classifying metastasis.

Data augmentation techniques improved model robustness.

Manual tumor annotation's impact was systematically assessed.

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.