Selective Cross-Modal Learning is introduced, a lightweight pre-fusion refinement framework that enriches unimodal embeddings by selectively incorporating complementary cues from concurrently available modalities and maintains its advantage under missing-pathology and missing-genomics conditions.
This work investigates whether image pretraining on 3D CTA scans reduces modality imbalance and improves cross-modal integration for stroke recurrence prediction, a clinically critical task recently addressed.
Christian Gapp, Elias Tappeiner, Martin Welk et al.· 0 citations
Multi-modal learning combining medical images and clinical text is promising for disease diagnosis. However, standard multi-modal training leads to shortcut learning: models exploit the easier modality (e.g., diagnostic cues in text) while neglecting harder-to-learn features (e.g., subtle visual patterns). We propose U...
Zijian Gu, Weikai Lin, Shuang Zhou et al.· 0 citations
Multimodal medical data provide complementary information for cancer diagnosis and prognosis; however, effectively integrating heterogeneous modalities remains challenging because real-world clinical datasets frequently contain incomplete modality observations. We present a unified benchmarking framework for multimodal...
Nhu Le Nguyen Quynh, Phuong Nguyen Ngoc Thanh, Tuan Le et al.· International Conference on...· 0 citations
In clinical practice, Magnetic Resonance Imaging (MRI) data frequently suffer from the absence of certain imaging modalities, which inevitably degrades predictive performance. Existing approaches typically treat different modalities as independent and non-interacting during modal feature extraction, despite the prese...
REM-Fuse is introduced, an evidential deep learning framework in which per-source Dirichlet uncertainty adaptively weights each source through dual-channel weighting, asymmetric cross-scale enhancement, and Dempster–Shafer-inspired evidence accumulation that provides a compact reliability-aware fusion strategy for canc...
Yalu Huang, Yushuai Yuan, Wenbin Ye et al.· Mathematics· 0 citations
Large Language Models (LLMs) have displayed impressive capabilities in handling tasks that require few demonstration examples, making them effective few-shot learners. Despite their potential, LLMs face challenges when it comes to addressing complex real-world tasks that involve multiple modalities or reasoning steps....
H. Mathavan, H. Liu· 0 citations
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