2 citations · 2 across the 13 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentation
Weiren Zhao, Lanfeng Zhong, Xin Liao +4
Semi-Supervised Learning (SSL) is important for reducing the annotation cost for medical image segmentation models. State-of-the-art SSL methods such as Mean Teacher, FixMatch and…
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…
SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation
Xinya Liu, Jianghao Wu, Tao Lu +2
Domain Adaptation (DA) is crucial for robust deployment of medical image segmentation models when applied to new clinical centers with significant domain shifts. Source-Free Domain…
Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification
Xiangyu Sun, Xiaoguang Zou, Yuanquan Wu +2
X-ray imaging is pivotal in medical diagnostics, offering non-invasive insights into a range of health conditions. Recently, vision-language models, such as the Contrastive Languag…
GLFC: Unified Global-Local Feature and Contrast Learning with Mamba-Enhanced UNet for Synthetic CT Generation from CBCT
Xianhao Zhou, Jianghao Wu, Huangxuan Zhao +3
Generating synthetic Computed Tomography (CT) images from Cone Beam Computed Tomography (CBCT) is desirable for improving the image quality of CBCT. Existing synthetic CT (sCT) gen…
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction
Tao Song, Yicheng Wu, Minhao Hu +6
Accelerated MRI reconstruction plays a vital role in reducing scan time while preserving image quality. While most existing methods rely on complex-valued image-space or k-space da…
Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI Synthesis
Tao Song, Yicheng Wu, Minhao Hu +6
Multimodal MR image synthesis aims to generate missing modality images by effectively fusing and mapping from a subset of available MRI modalities. Most existing methods adopt an i…
Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases
Xiangde Luo, Zihan Li, Shaoting Zhang +2
Deep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and…
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
Jianghao Wu, Dong Guo, Guotai Wang +4
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised D…