23 citations · 30 across the 6 of their papers we have counts for
7 papers
Revisiting Data Scaling in Medical Image Segmentation via Topology-Aware Augmentation
Yuetan Chu, Zhongyi Han, Gongning Luo +1
Understanding how segmentation performance scales with training data is fundamental for developing data-efficient medical AI systems. In this study, we systematically revisit data…
Facial Foundational Model Advances Early Warning of Coronary Artery Disease from Live Videos with DigitalShadow
Juexiao Zhou, Zhongyi Han, Mankun Xin +19
Global population aging presents increasing challenges to healthcare systems, with coronary artery disease (CAD) responsible for approximately 17.8 million deaths annually, making…
Improving Representation of High-frequency Components for Medical Visual Foundation Models
Yuetan Chu, Yilan Zhang, Zhongyi Han +5
Foundation models have recently attracted significant attention for their impressive generalizability across diverse downstream tasks. However, these models are demonstrated to exh…
SkinCaRe: A Multimodal Dermatology Dataset Annotated with Medical Caption and Chain-of-Thought Reasoning
Yuhao Shen, Liyuan Sun, Yan Xu +10
With the widespread application of artificial intelligence (AI), particularly deep learning (DL) and vision large language models (VLLMs), in skin disease diagnosis, the need for i…
EchoAlign: Bridging Generative and Discriminative Learning under Noisy Labels
Yuxiang Zheng, Zhongyi Han, Yilong Yin
Noisy labels severely hinder the accuracy and generalization of machine learning models, especially when ambiguous instance features make reliable annotation difficult. Existing ap…
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Recent studies indicate that large multimodal models (LMMs) potentially act as general-purpose assistants and are highly robust against different distributions. Despite this, domai…
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining…