Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound

TL;DR

This paper introduces an automated, reinforcement learning-based multimodal ultrasound classification system for breast cancer, effectively combining four ultrasound modalities to improve benign versus malignant nodule discrimination.

Contribution

It proposes a novel end-to-end framework that learns optimal modality weights automatically, enhancing classification accuracy without manual heuristics.

Findings

Achieved 95.4% classification accuracy.

Demonstrated effective multimodal integration.

Outperformed traditional weighting methods.

Abstract

Breast cancer is the most common invasive cancer in women. Besides the primary B-mode ultrasound screening, sonographers have explored the inclusion of Doppler, strain and shear-wave elasticity imaging to advance the diagnosis. However, recognizing useful patterns in all types of images and weighing up the significance of each modality can elude less-experienced clinicians. In this paper, we explore, for the first time, an automatic way to combine the four types of ultrasonography to discriminate between benign and malignant breast nodules. A novel multimodal network is proposed, along with promising learnability and simplicity to improve classification accuracy. The key is using a weight-sharing strategy to encourage interactions between modalities and adopting an additional cross-modalities objective to integrate global information. In contrast to hardcoding the weights of each modality in the model, we embed it in a Reinforcement Learning framework to learn this weighting in an end-to-end manner. Thus the model is trained to seek the optimal multimodal combination without handcrafted heuristics. The proposed framework is evaluated on a dataset contains 1616 set of multimodal images. Results showed that the model scored a high classification accuracy of 95.4%, which indicates the efficiency of the proposed method.