Zanting Ye — accepted AI-conference papers

2026

OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks

Zhihao Peng, Cheng Wang, Shengyuan Liu, Zhiying Liang, Zanting Ye, Min Jie Ju +2

CVPR 2026

Brain imaging analysis is crucial for diagnosing and treating brain disorders, and multimodal large language models (MLLMs) are increasingly supporting it. However, current brain imaging visual question-answering (VQA) benchmarks either cover a limited number of imaging modalities or are restricted

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2026

OralGPT-Omni: A Versatile Dental Multimodal Large Language Model

Jing Hao, Yuci Liang, Lizhuo Lin, Yuxuan Fan, Wenkai Zhou, Kaixin Guo +9

CVPR 2026

Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties, yet dentistry remains underexplored, in part due to limited domain-specific data, scarce dental expert annotations, insufficient modality-specific modeling, and challenges in reliability. I

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