Dynamic Uncertainty-Aware Adaptive Subspace Fusion Network for Robust Multimodal Medical Image Classification

Krishnakumar B (School of Computing, SASTRA Deemed University, Tamil Nadu, India.)
Thanga Parvathi (Department of Computer Technology, Bannari Amman Institute of Technology, Sathyamangalam, Erode, Tamilnadu, India.)
K. Nithya (Department of Artificial Intelligence and Data science, Karpagam Academy of Higher Education, Coimbatore, India.)
M. Pyingkodi (Department of Computer Applications, Kongu Engineering College, Perundurai, India)
Kunchanapalli Rama Krishna (Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District -522302, Andhra Pradesh)
Jeevitha R (Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India.)

Journal of Electronics, Electromedical Engineering, and Medical Informatics
Vol 8 No 2 (2026): April

Multimodal medical image classification leverages complementary information from multiple imaging modalities to improve diagnostic accuracy and clinical decision-making. However, most existing multimodal fusion approaches rely on deterministic low-rank constraints and assume equal importance across all modalities. Such assumptions significantly limit flexibility, robustness, and interpretability, particularly in real-world clinical scenarios where modality data may be noisy, incomplete, or partially missing. To address these challenges, this work proposes a Dynamic Uncertainty-Aware Adaptive Subspace Fusion Network (DUA-SFNet) for robust multimodal medical image classification. The core of the proposed framework is a rank-learning adaptive-rank tensor decomposition module that dynamically adjusts subspace dimensionality according to the intrinsic complexity of the input data. This adaptive mechanism effectively reduces feature redundancy while preserving the highly discriminative information essential for accurate classification. In addition, DUA-SFNet incorporates a modality uncertainty estimation scheme to explicitly quantify the reliability and trustworthiness of each modality. By assigning uncertainty-aware weights during the fusion process, the framework can suppress unreliable or noisy modalities while emphasizing more informative ones, thereby improving resilience under adverse data conditions. Furthermore, a hierarchical adaptive attention strategy is employed to jointly model intra-subspace feature interactions and inter-modality dependencies. This design enhances feature representation capability while offering improved clinical interpretability by revealing how different modalities and subspaces contribute to the final decision. Extensive experiments conducted on multiple public and self-organized multimodal medical image datasets demonstrate that DUA-SFNet consistently outperforms state-of-the-art methods, achieving classification accuracy improvements of 3.8–6.2% and F1-score gains of 4.1–7.5%. Overall, DUA-SFNet provides an interpretable, uncertainty-aware, and adaptive solution for next-generation multimodal medical image analysis.