most citedDino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

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cs.CV2025★ 1 cited

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

Haoyue Li, Yifan Gao, Feng Yuan +2

Foundation models pre-trained on large-scale natural image datasets offer a powerful paradigm for medical image segmentation. However, effectively transferring their learned repres…

cs.CV2025

PRISM: A Framework Harnessing Unsupervised Visual Representations and Textual Prompts for Explainable MACE Survival Prediction from Cardiac Cine MRI

Haoyang Su, Jin-Yi Xiang, Shaohao Rui +6

Accurate prediction of major adverse cardiac events (MACE) remains a central challenge in cardiovascular prognosis. We present PRISM (Prompt-guided Representation Integration for S…