Joint Stream: Malignant Region Learning for Breast Cancer Diagnosis

TL;DR

This paper introduces a novel malignant region learning attention network that classifies six key factors in breast cancer diagnosis from whole slide images, improving multi-factor prediction accuracy.

Contribution

It proposes a multi-factor classification method using spatial and frequency domain information with an attention network for better breast cancer diagnosis.

Findings

Significant improvement in classification performance on public datasets.

Effective integration of spatial and frequency information.

Enhanced multi-factor and single-factor prediction accuracy.

Abstract

Early diagnosis of breast cancer (BC) significantly contributes to reducing the mortality rate worldwide. The detection of different factors and biomarkers such as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal growth factor receptor 2 (HER2) gene, Histological grade (HG), Auxiliary lymph node (ALN) status, and Molecular subtype (MS) can play a significant role in improved BC diagnosis. However, the existing methods predict only a single factor which makes them less suitable to use in diagnosis and designing a strategy for treatment. In this paper, we propose to classify the six essential indicating factors (ER, PR, HER2, ALN, HG, MS) for early BC diagnosis using H\&E stained WSI's. To precisely capture local neighboring relationships, we use spatial and frequency domain information from the large patch size of WSI's malignant regions. Furthermore, to cater the variable number of regions of interest sizes and give due attention to each region, we propose a malignant region learning attention network. Our experimental results demonstrate that combining spatial and frequency information using the malignant region learning module significantly improves multi-factor and single-factor classification performance on publicly available datasets.