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

1 citations · 1 across the 4 of their papers we have counts for

collaborators

9 papers

eess.IV2026

Colon-Bench: An Agentic Workflow for Scalable Dense Lesion Annotation in Full-Procedure Colonoscopy Videos

Abdullah Hamdi, Changchun Yang, Xin Gao

Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the lack of densely annotated, long-sequenc…

cs.CV2026

Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis

Yiheng Cao, Gustavo Andrade-Miranda, Jiatian Zhang +2

Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions.…

cs.CV2026

Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling

Yiheng Cao, Gustavo Andrade-Miranda, Jiatian Zhang +2

Cine cardiac magnetic resonance is the gold standard for assessing cardiac function, but the scarcity of public datasets limits the development of advanced data-driven models. To a…

cs.CV20261 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.CV2026

GIFT: Global Irreplaceability Frame Targeting for Efficient Video Understanding

Junpeng Ma, Sashuai Zhou, Guanghao Li +9

Video Large Language Models (VLMs) have achieved remarkable success in video understanding, but the significant computational cost from processing dense frames severely limits thei…

cs.CV2025

Med-K2N: Flexible K-to-N Modality Translation for Medical Image Synthesis

Feng Yuan, Yifan Gao, Yuehua Ye +2

Cross-modal medical image synthesis research focuses on reconstructing missing imaging modalities from available ones to support clinical diagnosis. Driven by clinical necessities…