Multimodal Fusion Learning with Dual Attention for Medical Imaging

• URL: http://arxiv.org/abs/2412.01248v1
• Date: Mon, 02 Dec 2024 08:11:12 GMT
• Title: Multimodal Fusion Learning with Dual Attention for Medical Imaging
• Authors: Joy Dhar, Nayyar Zaidi, Maryam Haghighat, Puneet Goyal, Sudipta Roy, Azadeh Alavi, Vikas Kumar,
• Abstract summary: Multimodal fusion learning has shown significant promise in classifying various diseases such as skin cancer and brain tumors. Existing methods face three key limitations. DRIFA can be integrated with any deep neural network, forming a multimodal fusion learning framework denoted as DRIFA-Net.
• Score: 8.74917075651321
• License:
• Abstract: Multimodal fusion learning has shown significant promise in classifying various diseases such as skin cancer and brain tumors. However, existing methods face three key limitations. First, they often lack generalizability to other diagnosis tasks due to their focus on a particular disease. Second, they do not fully leverage multiple health records from diverse modalities to learn robust complementary information. And finally, they typically rely on a single attention mechanism, missing the benefits of multiple attention strategies within and across various modalities. To address these issues, this paper proposes a dual robust information fusion attention mechanism (DRIFA) that leverages two attention modules, i.e. multi-branch fusion attention module and the multimodal information fusion attention module. DRIFA can be integrated with any deep neural network, forming a multimodal fusion learning framework denoted as DRIFA-Net. We show that the multi-branch fusion attention of DRIFA learns enhanced representations for each modality, such as dermoscopy, pap smear, MRI, and CT-scan, whereas multimodal information fusion attention module learns more refined multimodal shared representations, improving the network's generalization across multiple tasks and enhancing overall performance. Additionally, to estimate the uncertainty of DRIFA-Net predictions, we have employed an ensemble Monte Carlo dropout strategy. Extensive experiments on five publicly available datasets with diverse modalities demonstrate that our approach consistently outperforms state-of-the-art methods. The code is available at https://github.com/misti1203/DRIFA-Net.

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