20 citations · 22 across the 13 of their papers we have counts for
13 papers
Volumetric Radiology AI in the Era of Multimodal Large Language Models
Zanting Ye, Shengyuan Liu, Xin Liu +16
Advances in multimodal large language models (MLLMs) are extending radiological artificial intelligence (AI) beyond task-specific image analysis toward multimodal understanding and…
OralAgent: Integrating Reasoning, Tools, and Knowledge for Interactive Dental Image Analysis
Jing Hao, Siyuan Dai, Yongxin Zhang +13
Dental image analysis plays a pivotal role in supporting accurate diagnosis and treatment planning in oral healthcare. Although recent advances have produced dental AI models for s…
Topology-Guided Biomechanical Profiling: A White-Box Framework for Opportunistic Screening of Spinal Instability on Routine CT
Zanting Ye, Xuanbin Wu, Guoqing Zhong +9
Routine oncologic computed tomography (CT) presents an ideal opportunity for screening spinal instability, yet prophylactic stabilization windows are frequently missed due to the c…
InViC: Intent-aware Visual Cues for Medical Visual Question Answering
Zhisong Wang, Ziyang Chen, Zanting Ye +3
Medical visual question answering (Med-VQA) aims to answer clinically relevant questions grounded in medical images. However, existing multimodal large language models (MLLMs) ofte…
MedPruner: Training-Free Hierarchical Token Pruning for Efficient 3D Medical Image Understanding in Vision-Language Models
Shengyuan Liu, Zanting Ye, Yunrui Lin +6
While specialized Medical Vision-Language Models (VLMs) have achieved remarkable success in interpreting 2D and 3D medical modalities, their deployment for 3D volumetric data remai…
Unveiling and Bridging the Functional Perception Gap in MLLMs: Atomic Visual Alignment and Hierarchical Evaluation via PET-Bench
Zanting Ye, Xiaolong Niu, Xuanbin Wu +14
While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in tasks such as abnormality detection and report generation for anatomical modalities, thei…
OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks
Zhihao Peng, Cheng Wang, Shengyuan Liu +5
Brain imaging analysis is crucial for diagnosing and treating brain disorders, and multimodal large language models (MLLMs) are increasingly supporting it. However, current brain i…
OralGPT-Omni: A Versatile Dental Multimodal Large Language Model
Jing Hao, Yuci Liang, Lizhuo Lin +12
Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties; yet, dentistry remains underexplored, in part due to limited domain-s…
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising
Xiaolong Niu, Zanting Ye, Xu Han +4
Acquiring high-quality Positron Emission Tomography (PET) images requires administering high-dose radiotracers, which increases radiation exposure risks. Generating standard-dose P…
Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentation
Zanting Ye, Xiaolong Niu, Xuanbin Wu +3
Deep learning-based medical image segmentation has shown remarkable success; however, it typically requires extensive pixel-level annotations, which are both expensive and time-int…
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
Zanting Ye, Xiaolong Niu, Xu Han +7
Organ segmentation in Positron Emission Tomography (PET) plays a vital role in cancer quantification. Low-dose PET (LDPET) provides a safer alternative by reducing radiation exposu…
FSDA-DG: Improving Cross-Domain Generalizability of Medical Image Segmentation with Few Source Domain Annotations
Zanting Ye, Ke Wang, Wenbing Lv +2
Deep learning-based medical image segmentation faces significant challenges arising from limited labeled data and domain shifts. While prior approaches have primarily addressed the…
MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization
Ke Wang, Zanting Ye, Xiang Xie +3
Accurate segmentation of clustered microcalcifications in mammography is crucial for the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy, recent…