5 citations · 5 across the 27 of their papers we have counts for
10 papers · 1 filter
MorVess: Morphology-Aware Pulmonary Vessel Segmentation Network
Fuyou Mao, Yifei Chen, Beining Wu +9
Accurate pulmonary vessel segmentation remains challenging due to the sparse, tortuous, and multi-scale nature of vascular structures, where small branches are easily lost and topo…
HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT
Numan Saeed, Salma Hassan, Shahad Hardan +27
Head and neck cancers (HNC) represent a significant global health burden, with accurate tumor delineation being essential for effective radiotherapy planning. The complexity of the…
Mutual Distillation of Dual-Foundation Models for Semi-Supervised PET/CT Segmentation
Fuyou Mao, Beining Wu, Yanfeng Jiang +5
Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology. To ease the high annotation cost of PET/CT segmentation, semi-supervised…
GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT
Shuo Jiang, Yuhao Hong, Chunbo Jiang +9
Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation, yet remains challenging due to the semantic-spatial gap between free-t…
R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound
Yuanhan Wang, Yifei Chen, Beining Wu +7
Accurate estimation of the Angle of Progression (AoP) from intrapartum transperineal ultrasound is critical for objective assessment of labor progression, yet remains highly sensit…
TSM-Pose: Topology-Aware Learning with Semantic Mamba for Category-Level Object Pose Estimation
Jinshuo Liu, Bingtao Ma, Junlin Su +6
Category-level object pose estimation is fundamental for embodied intelligence, yet achieving robust generalization to unseen instances remains challenging. However, existing metho…
AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI
Changwei Wu, Yifei Chen, Yuxin Du +7
Reliable anomaly detection in brain MRI remains challenging due to the scarcity of annotated abnormal cases and the frequent absence of key imaging modalities in real clinical work…
A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation
Wenjing Yu, Shuo Jiang, Yifei Chen +9
Test time Adaptation is a promising approach for mitigating domain shift in medical image segmentation; however, current evaluations remain limited in terms of modality coverage, t…
SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI
Yuanhan Wang, Yifei Chen, Shuo Jiang +7
Reliable brain tumor segmentation in MRI is indispensable for treatment planning and outcome monitoring, yet models trained on curated benchmarks often fail under domain shifts ari…
Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentation
Fuyou Mao, Beining Wu, Yanfeng Jiang +3
Ambiguity in medical image segmentation calls for models that capture full conditional distributions rather than a single point estimate. We present Prior-Guided Residual Diffusion…