4 citations · 12 across the 12 of their papers we have counts for
13 papers
FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
Guofeng Zhang, Rong Han, Xiaoyu Wang +4
Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, extending generative models fr…
Towards World Models in Biomedical Research
Guangyu Wang, Jingkun Yue, Siqi Zhang +19
A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and…
MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains
Leyan Xue, Changqing Zhang, Kecheng Xue +3
Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Current fusion methods are typically e…
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding
Jingkun Yue, Siqi Zhang, Zinan Jia +4
Visual grounding is essential for precise perception and reasoning in multimodal large language models (MLLMs), especially in medical imaging domains. While existing medical visual…
Learning to erase quantum states: thermodynamic implications of quantum learning theory
Haimeng Zhao, Yuzhen Zhang, John Preskill
The energy cost of erasing quantum states depends on our knowledge of the states. We show that learning algorithms can acquire such knowledge to erase many copies of an unknown sta…
Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation
Xiaohong Liu, Guoxing Yang, Yulin Luo +8
Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in medicine have shown potential in p…
CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction
Rong Han, Xiaohong Liu, Tong Pan +8
Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity predictio…
MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks
Haijiang Tian, Jingkun Yue, Xiaohong Liu +3
Medical images are often more difficult to acquire than natural images due to the specialism of the equipment and technology, which leads to less medical image datasets. So it is h…
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning
Yuning Yang, Han Yu, Chuan Sun +5
Federated Learning (FL) is a collaborative machine learning paradigm for training models on local sensitive data with privacy protection. Pre-trained transformer-based models have…
A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis
Linxi Zhao, Jiankai Tang, Dongyu Chen +5
Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present…
TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine
Guoxing Yang, Jianyu Shi, Zan Wang +2
Pre-training and fine-tuning have emerged as a promising paradigm across various natural language processing (NLP) tasks. The effectiveness of pretrained large language models (LLM…
TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution
Shan Huang, Xiaohong Liu, Tao Tan +4
Magnetic resonance images (MRI) acquired with low through-plane resolution compromise time and cost. The poor resolution in one orientation is insufficient to meet the requirement…
Self adaptive global-local feature enhancement for radiology report generation
Yuhao Wang, Kai Wang, Xiaohong Liu +3
Automated radiology report generation aims at automatically generating a detailed description of medical images, which can greatly alleviate the workload of radiologists and provid…