17 papers
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…
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…
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…
VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Annotation-Free Pathological Image Classification
Lanfeng Zhong, Zongyao Huang, Yang Liu +4
Classification of pathological images is the basis for automatic cancer diagnosis. Despite that deep learning methods have achieved remarkable performance, they heavily rely on lab…