papers

Publications (12)

eess.IV2025

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…

cs.CV2025

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…

cs.CV2026

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…

cs.CV2026

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…

q-bio.TO2025

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…

cs.CL2026

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…