5 citations · 13 across the 13 of their papers we have counts for
14 papers
Beyond Masks and Trajectories: Flow-Guided Latent Action Injection for Stable Surgical Video Generation
Tsz-Yui Qin, Siyu Zhou, Chi-Keung Tang +2
Surgical video generation holds substantial potential for surgical education, simulation, and data augmentation, yet generating surgical videos with realistic and clinically plausi…
A Multimodal Agentic Pathology Co-pilot via Evidence Grounded Reasoning
Zhe Xu, Zhengyu Zhang, Zhiyuan Cai +26
Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the potential to…
Large-scale Self-supervised Video Foundation Model for Intelligent Surgery
Shu Yang, Fengtao Zhou, Leon Mayer +16
Computer-Assisted Intervention (CAI) has the potential to revolutionize modern surgery, with surgical scene understanding serving as a critical component in supporting decision-mak…
MedBookVQA: A Systematic and Comprehensive Medical Benchmark Derived from Open-Access Book
Sau Lai Yip, Sunan He, Yuxiang Nie +4
The accelerating development of general medical artificial intelligence (GMAI), powered by multimodal large language models (MLLMs), offers transformative potential for addressing…
UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
Linshan Wu, Yuxiang Nie, Sunan He +12
The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable to clinicians. Howev…
An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training
Yuxiang Nie, Sunan He, Yequan Bie +14
The clinical adoption of artificial intelligence (AI) in medical imaging requires models that are both diagnostically accurate and interpretable to clinicians. While current multim…
Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
Yuting He, Fuxiang Huang, Xinrui Jiang +4
Foundation model, which is pre-trained on broad data and is able to adapt to a wide range of tasks, is advancing healthcare. It promotes the development of healthcare artificial in…
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Sunan He, Yuxiang Nie, Hongmei Wang +21
Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of m…
Mix-Initiative Response Generation with Dynamic Prefix Tuning
Yuxiang Nie, Heyan Huang, Xian-Ling Mao +1
Mixed initiative serves as one of the key factors in controlling conversation directions. For a speaker, responding passively or leading proactively would result in rather differen…
SciMRC: Multi-perspective Scientific Machine Reading Comprehension
Xiao Zhang, Heqi Zheng, Yuxiang Nie +2
Scientific machine reading comprehension (SMRC) aims to understand scientific texts through interactions with humans by given questions. As far as we know, there is only one datase…
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking
Yuxiang Nie, Heyan Huang, Wei Wei +1
Annotating long-document question answering (long-document QA) pairs is time-consuming and expensive. To alleviate the problem, it might be possible to generate long-document QA pa…
Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network
Yuxiang Nie, Heyan Huang, Wei Wei +1
Long document question answering is a challenging task due to its demands for complex reasoning over long text. Previous works usually take long documents as non-structured flat te…
Unsupervised Question Answering via Answer Diversifying
Yuxiang Nie, Heyan Huang, Zewen Chi +1
Unsupervised question answering is an attractive task due to its independence on labeled data. Previous works usually make use of heuristic rules as well as pre-trained models to c…
Multi-task Learning for Low-resource Second Language Acquisition Modeling
Yong Hu, Heyan Huang, Tian Lan +5
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned. It is a fundamen…