SimMLM: A Simple Framework for Multi-modal Learning with Missing Modality
This addresses the problem of handling incomplete multimodal data in applications like medical imaging and classification, offering a robust solution that is incremental but effective.
The paper tackles multimodal learning with missing modalities by proposing SimMLM, a framework that uses a dynamic gating mechanism and a novel ranking loss to maintain or improve accuracy as more modalities become available, achieving superior performance on medical image segmentation and classification tasks compared to existing methods.
In this paper, we propose SimMLM, a simple yet powerful framework for multimodal learning with missing modalities. Unlike existing approaches that rely on sophisticated network architectures or complex data imputation techniques, SimMLM provides a generic and effective solution that can adapt to various missing modality scenarios with improved accuracy and robustness. Specifically, SimMLM consists of a generic Dynamic Mixture of Modality Experts (DMoME) architecture, featuring a dynamic, learnable gating mechanism that automatically adjusts each modality's contribution in both full and partial modality settings. A key innovation of SimMLM is the proposed More vs. Fewer (MoFe) ranking loss, which ensures that task accuracy improves or remains stable as more modalities are made available. This aligns the model with an intuitive principle: removing one or more modalities should not increase accuracy. We validate SimMLM on multimodal medical image segmentation (BraTS 2018) and multimodal classification (UPMC Food-101, avMNIST) tasks, where it consistently surpasses competitive methods, demonstrating superior accuracy, interpretability, robustness, and reliability across both complete and missing modality scenarios at test time.