collaborators

17 papers

cs.CV2026

Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction

Jingxian Xu, Yuhao Huang, Rusi Chen +2

Accurate landmark localization in medical images is a fundamental step for quantitative clinical measurement and downstream analysis. Existing localization methods have advanced, a…

cs.CV2026

Recurrent Contrastive Learning for Imbalanced Medical Image Classification

Zhiyuan Zhu, Xinling Meng, Junxuan Yu +15

Medical image classification often suffers from class imbalance due to the inherent disparities in disease incidence. Existing approaches, such as class resampling and loss reweigh…

cs.CV2026

ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction

Yaofei Duan, Yuhao Huang, Tianyu Zhang +12

Neoadjuvant chemotherapy (NAC) response prediction is clinically important for treatment stratification in breast cancer. However, robust pre-treatment pathological complete respon…

cs.CV2026

FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection

Rusi Chen, Yuhao Huang, Hongyuan Zhang +4

Accurate detection of end-systole (ES) and end-diastole (ED) frames is fundamental to echocardiographic assessment. Existing methods are typically developed in a view-specific mann…

cs.CV2026

Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation

Le Ou, Xiliang Zhu, Huanwen Liang +8

Accurate fetal birth weight (FBW) estimation shortly before delivery is clinically valuable yet challenging due to its reliance on operator expertise, particularly in low-resource…

cs.CV2026

Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound

Huanwen Liang, Yuhao Huang, Xiliang Zhu +6

Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management. Although ultrasound (US) is the primary modality for prenatal…