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.

翻译：多模态融合学习通过整合来自不同成像模态（如MRI、CT、SPECT）的异构数据，在解决皮肤癌和脑肿瘤预测等医学问题方面展现出巨大潜力。然而，现有MFL方法面临三个关键局限：a) 通常专精于特定模态，忽略了跨多样模态的有效共享互补信息，从而限制了其在多疾病分析中的泛化能力；b) 依赖计算成本高昂的模型，制约了其在资源受限环境中的适用性；c) 缺乏对抗对抗攻击的鲁棒性，影响了医学AI应用的可靠性。为应对这些局限，我们提出一种新颖的多注意力集成学习网络，包含两个核心组件：a) 用于捕捉精细化模态特定多尺度模式的高效残差学习注意力模块；b) 用于学习跨多样模态的丰富互补共享表征的高效多模态交叉注意力模块。此外，为确保对抗鲁棒性，我们通过引入随机投影滤波器和调制注意力噪声，将MAIL网络扩展为鲁棒MAIL网络。在20个公开数据集上的大量评估表明，MAIL与鲁棒MAIL均优于现有方法，最高可实现9.34%的性能提升，同时降低高达78.3%的计算成本。这些结果凸显了我们方法的优越性，能够提供比顶尖竞争方法更可靠的预测。代码：https://github.com/misti1203/MAIL-Robust-MAIL。