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cs.CV2026

Incentivizing Cardiologist-Like Reasoning in MLLMs for Interpretable Echocardiographic Diagnosis

Yi Qin, Lehan Wang, Chenxu Zhao +2

Echocardiographic diagnosis is vital for cardiac screening yet remains challenging. Existing echocardiography foundation models do not effectively capture the relationships between…

cs.CV2025

Proactive Reasoning-with-Retrieval Framework for Medical Multimodal Large Language Models

Lehan Wang, Yi Qin, Honglong Yang +1

Incentivizing the reasoning ability of Multimodal Large Language Models (MLLMs) is essential for medical applications to transparently analyze medical scans and provide reliable di…

cs.CV2025

VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction

Lehan Wang, Hualiang Wang, Chubin Ou +3

Cardiovascular disease (CVD) remains the leading cause of death worldwide, requiring urgent development of effective risk assessment methods for timely intervention. While current…

cs.CV2025

Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction

Haonan Wang, Qixiang Zhang, Lehan Wang +2

Decoding visual stimuli from neural activity is essential for understanding the human brain. While fMRI methods have successfully reconstructed static images, fMRI-to-video reconst…

cs.CV2025

Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration

Honglong Yang, Shanshan Song, Yi Qin +6

Generalist Medical AI (GMAI) systems have demonstrated expert-level performance in biomedical perception tasks, yet their clinical utility remains limited by inadequate multi-modal…

cs.CV2025

Reinforced Correlation Between Vision and Language for Precise Medical AI Assistant

Haonan Wang, Jiaji Mao, Lehan Wang +11

Medical AI assistants support doctors in disease diagnosis, medical image analysis, and report generation. However, they still face significant challenges in clinical use, includin…