Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-t

arXiv:2606.17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to

arXiv:2609.12035v1 Announce Type: new Abstract: Cardiovascular diagnosis rests on integrating complementary modalities, like ECG, echocardiography, chest radiographs, and clinical

arXiv:2603.22372v1 Announce Type: cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time

arXiv:2609.04504v1 Announce Type: new Abstract: Cardiac, neural, behavioral, and speech measurements from wearable and mobile devices provide partial, noise-sensitive views of

arXiv:2603.22721v1 Announce Type: new Abstract: Recent progress in artificial intelligence has encouraged numerous attempts to understand and decode human visual system from brain

arXiv:2606.17437v1 Announce Type: cross Abstract: Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main

arXiv:2609.04804v1 Announce Type: new Abstract: Synthetic medical time-series generation can alleviate data scarcity and support the development of reliable clinical prediction

arXiv:2603.18462v1 Announce Type: new Abstract: In the era of large-scale pre-trained models, effectively adapting general knowledge to specific affective computing tasks remains