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most citedDescribe Anything in Medical Images

2 citations · 7 across the 18 of their papers we have counts for

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Showing 2025 · cs.CVShow all

5 papers · 2 filters

cs.CV2025★ 1 cited

Prompt-based Adaptation in Large-scale Vision Models: A Survey

Xi Xiao, Yunbei Zhang, Lin Zhao +12

In computer vision, Visual Prompting (VP) and Visual Prompt Tuning (VPT) have recently emerged as lightweight and effective alternatives to full fine-tuning for adapting large-scal…

cs.CV2025

SOFA: Deep Learning Framework for Simulating and Optimizing Atrial Fibrillation Ablation

Yunsung Chung, Chanho Lim, Ghassan Bidaoui +3

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated with catheter ablation procedures, but procedural outcomes are highly variable. Evaluating and improving ab…

cs.CV2025

Visual Instance-aware Prompt Tuning

Xi Xiao, Yunbei Zhang, Xingjian Li +5

Visual Prompt Tuning (VPT) has emerged as a parameter-efficient fine-tuning paradigm for vision transformers, with conventional approaches utilizing dataset-level prompts that rema…

cs.CV2025

Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback

Janet Wang, Yunbei Zhang, Zhengming Ding +1

Paucity of medical data severely limits the generalizability of diagnostic ML models, as the full spectrum of disease variability can not be represented by a small clinical dataset…

cs.CV2025★ 2 cited

Describe Anything in Medical Images

Xi Xiao, Yunbei Zhang, Thanh-Huy Nguyen +10

Localized image captioning has made significant progress with models like the Describe Anything Model (DAM), which can generate detailed region-specific descriptions without explic…