Effective and Robust Multimodal Medical Image Analysis

TL;DR

This paper introduces MAIL and Robust-MAIL, innovative multimodal fusion networks for medical image analysis that improve accuracy, efficiency, and robustness against adversarial attacks across diverse datasets.

Contribution

The paper presents a novel Multi-Attention Integration Learning framework with efficient modules for shared and modality-specific feature extraction, enhancing generalizability and robustness in multimodal medical imaging.

Findings

Outperforms existing methods with up to 9.34% accuracy gain

Reduces computational costs by up to 78.3%

Enhances robustness against adversarial attacks

Abstract

Multimodal Fusion Learning (MFL), leveraging disparate data from various imaging modalities (e.g., MRI, CT, SPECT), has shown great potential for addressing medical problems such as skin cancer and brain tumor prediction. However, existing MFL methods face three key limitations: a) they often specialize in specific modalities, and overlook effective shared complementary information across diverse modalities, hence limiting their generalizability for multi-disease analysis; b) they rely on computationally expensive models, restricting their applicability in resource-limited settings; and c) they lack robustness against adversarial attacks, compromising reliability in medical AI applications. To address these limitations, we propose a novel Multi-Attention Integration Learning (MAIL) network, incorporating two key components: a) an efficient residual learning attention block for capturing refined modality-specific multi-scale patterns and b) an efficient multimodal cross-attention module for learning enriched complementary shared representations across diverse modalities. Furthermore, to ensure adversarial robustness, we extend MAIL network to design Robust-MAIL by incorporating random projection filters and modulated attention noise. Extensive evaluations on 20 public datasets show that both MAIL and Robust-MAIL outperform existing methods, achieving performance gains of up to 9.34% while reducing computational costs by up to 78.3%. These results highlight the superiority of our approaches, ensuring more reliable predictions than top competitors. Code: https://github.com/misti1203/MAIL-Robust-MAIL.