Globally-Aware Multiple Instance Classifier for Breast Cancer Screening

TL;DR

This paper introduces a neural network that combines global and local image information to classify breast cancer in mammograms, achieving radiologist-level accuracy and providing localization of malignant regions.

Contribution

It presents a novel globally-aware multiple instance classifier that leverages both global saliency and local patches, outperforming baseline models in breast cancer screening.

Findings

Outperforms ResNet-based baseline models.

Achieves radiologist-level performance.

Generates pixel-level saliency maps for localization.

Abstract

Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller regions of interest. Moreover, both the global structure and local details play important roles in medical image analysis tasks. To address these unique properties of medical images, we propose a neural network that is able to classify breast cancer lesions utilizing information from both a global saliency map and multiple local patches. The proposed model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with image-level labels, it is able to generate pixel-level saliency maps that provide localization of possible malignant findings.