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cs.CV2025
Confounder-Aware Medical Data Selection for Fine-Tuning Pretrained Vision Models
Anyang Ji, Qingbo Kang, Wei Xu +3
The emergence of large-scale pre-trained vision foundation models has greatly advanced the medical imaging field through the pre-training and fine-tuning paradigm. However, selecti…
cs.CV2025
Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations
Kangyu Zhu, Ziyuan Qin, Huahui Yi +4
While mainstream vision-language models (VLMs) have advanced rapidly in understanding image level information, they still lack the ability to focus on specific areas designated by…
cs.CV2025
One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection
Yiyue Li, Shaoting Zhang, Kang Li +1
Traditional Anomaly Detection (AD) methods have predominantly relied on unsupervised learning from extensive normal data. Recent AD methods have evolved with the advent of large pr…