1 citations · 1 across the 15 of their papers we have counts for
18 papers
VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation
Zimu Zhang, Yiheng Zhong, Zhuoru Zhang +4
Semi-supervised 3D medical image segmentation reduces the need for dense voxel-level annotations by exploiting unlabeled volumes. Although existing methods such as consistency regu…
SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation
Zhuoru Zhang, Yiheng Zhong, Zimu Zhang +1
Recent advances in semi-supervised medical image segmentation have achieved remarkable performance through prediction consistency, pseudo-label supervision, and hard-region supervi…
HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation
Yingzhen Hu, Yiheng Zhong, Keying Zhu +5
Prompt-free adaptation of the Segment Anything Model (SAM) has emerged as a promising paradigm for automatic medical image segmentation. Existing methods mainly focus on prompt gen…
Attention Consistent Longitudinal Medical Visual Question Answering Guided by Vision Foundation Models
Jialin Wu, Qianru Zhang, Georges El Fakhri +1
Longitudinal medical visual question answering (VQA) requires reasoning about anatomical differences between an image of a current time point and an image of a referred time point.…
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation
Xiaofeng Liu, Qianru Zhang, Thibault Marin +4
The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic i…
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning
Yuan Zhao, Youwei Pang, Jiaming Zuo +10
Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the notion of a concept remains…
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models
Nhan Ho, Luu Le, Thanh-Huy Nguyen +3
Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge for foundation segmentation m…
Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift
Xiaofeng Liu, Menghua Xia, Yanis Chemli +3
In this work, we propose an unsupervised domain adaptation (UDA) framework for 3D volumetric lesion detection that adapts a detector trained on labeled FDG PET/CT to unlabeled PSMA…
Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation
Yingxue Su, Yiheng Zhong, Keying Zhu +7
Medical image segmentation is critical for computer-aided diagnosis. However, dense pixel-level annotation is time-consuming and costly, and medical datasets often exhibit severe c…
Rethinking Evaluation of Infrared Small Target Detection
Youwei Pang, Xiaoqi Zhao, Lihe Zhang +4
As an essential vision task, infrared small target detection (IRSTD) has seen significant advancements through deep learning. However, critical limitations in current evaluation pr…
UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation
Xiaoqi Zhao, Youwei Pang, Chenyang Yu +5
Multi-modal image segmentation faces real-world deployment challenges from incomplete/corrupted modalities degrading performance. While existing methods address training-inference…
Power Battery Detection
Xiaoqi Zhao, Peiqian Cao, Chenyang Yu +10
Power batteries are essential components in electric vehicles, where internal structural defects can pose serious safety risks. We conduct a comprehensive study on a new task, powe…
On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report)
Menghua Xia, Reimund Bayerlein, Yanis Chemli +7
Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as im…
Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation
Jia Wei, Xiaoqi Zhao, Jonghye Woo +4
Single domain generalization (SDG) has recently attracted growing attention in medical image segmentation. One promising strategy for SDG is to leverage consistent semantic shape p…
Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes
Xiaoqi Zhao, Youwei Pang, Shijie Chang +10
As large-scale foundation models trained on billions of image--mask pairs covering a vast diversity of scenes, objects, and contexts, SAM and its upgraded version, SAM~2, have sign…
Point-supervised Brain Tumor Segmentation with Box-prompted MedSAM
Xiaofeng Liu, Jonghye Woo, Chao Ma +2
Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly…
Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views
Jihoon Cho, Suhyun Ahn, Beomju Kim +8
Deep learning-based segmentation techniques have shown remarkable performance in brain segmentation, yet their success hinges on the availability of extensive labeled training data…
Anatomy-based quality metric of diffusion-weighted MRI data for accurate derivation of muscle fiber orientation
Nadya Shusharina, Xiaofeng Liu, Evangelia Kaza +3
Diffusion-weighted MRI (DW-MRI) is used to quantitatively characterize the microscopic structure of soft tissue due to the anisotropic diffusion of water in muscle. Applications su…