collaborators

12 papers

cs.CV2026

UniCycleFlow: Bidirectional Unpaired Image Translation with a Shared Rectified Flow

Xianhao Zhou, Jianghao Wu, Shaoting Zhang +1

Bidirectional unpaired image translation must preserve source-specific structure while learning coherent transformations in both directions without paired supervision. Existing met…

cs.CV2026

Concept Alignment Contrast and Long-Short Prompt Memory for Test-Time Adaptation of SAM3 in Medical Image Segmentation

Yubo Zhou, Jianghao Wu, Ping Ye +2

Concept segmentation models like Segment Anything Model 3 (SAM3) show strong generalization on natural images, yet their performance degrades in medical imaging due to the domain g…

cs.CV2026

A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation

Jianghao Wu, Xiangde Luo, Yubo Zhou +3

Test-Time Adaptation (TTA) offers a practical solution for deploying image segmentation models under domain shift without accessing source data or retraining. Among existing TTA st…

eess.IV2026

SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

Jia Fu, Litingyu Wang, He Li +27

Accurate delineation of Gross Tumor Volume (GTV), Lymph Node Clinical Target Volume (LN CTV), and Organ-at-Risk (OAR) from Computed Tomography (CT) scans is essential for precise r…

cs.CV2025

DINOv3-Guided Cross Fusion Framework for Semantic-aware CT generation from MRI and CBCT

Xianhao Zhou, Jianghao Wu, Ku Zhao +5

Generating synthetic CT images from CBCT or MRI has a potential for efficient radiation dose planning and adaptive radiotherapy. However, existing CNN-based models lack global sema…

cs.CV2025

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis

Ning Zhu, Xiaochuan Ma, Shaoting Zhang +1

Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model perfor…