16 citations · 31 across the 18 of their papers we have counts for
18 papers
Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis
Minheng Chen, Tong Chen, Chao Cao +4
Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroimaging-based brain network analy…
Large Language Models for Assisting American College Applications
Zhengliang Liu, Weihang You, Peng Shu +14
American college applications require students to navigate fragmented admissions policies, repetitive and conditional forms, and ambiguous questions that often demand cross-referen…
Gyral-Sulcal-Net: An Integrated Network Representation of Brain Folding Patterns
Chao Cao, Tong Chen, Nan Zhao +7
Our brain functions as a complex communication network, and studying it from a network perspective offers valuable insights into its organizational principles and links to cognitiv…
Adapting Large Language Models to Low-Resource Tibetan: A Two-Stage Continual and Supervised Fine-Tuning Study
Lifeng Chen, Ryan Lai, Tianming Liu
Adapting large language models (LLMs) to low-resource languages remains a major challenge due to data scarcity and cross-lingual drift. This work presents a two-stage adaptation of…
Build AI Assistants using Large Language Models and Agents to Enhance the Engineering Education of Biomechanics
Hanzhi Yan, Qin Lu, Xianqiao Wang +3
While large language models (LLMs) have demonstrated remarkable versatility across a wide range of general tasks, their effectiveness often diminishes in domain-specific applicatio…
Voxel-Level Brain States Prediction Using Swin Transformer
Yifei Sun, Daniel Chahine, Qinghao Wen +5
Understanding brain dynamics is important for neuroscience and mental health. Functional magnetic resonance imaging (fMRI) enables the measurement of neural activities through bloo…
Towards a general-purpose foundation model for fMRI analysis
Cheng Wang, Yu Jiang, Zhihao Peng +18
Functional MRI (fMRI) is crucial for studying brain function and diagnosing neurological disorders. However, existing analysis methods suffer from reproducibility and transferabili…
MolQAE: Quantum Autoencoder for Molecular Representation Learning
Yi Pan, Hanqi Jiang, Wei Ruan +5
We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to qu…
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Bang Liu, Xinfeng Li, Jiayi Zhang +45
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated…
Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology
Peilong Wang, Zhengliang Liu, Yiwei Li +12
Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable cap…
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
Yi Pan, Hanqi Jiang, Junhao Chen +10
Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implem…
ECHOPulse: ECG controlled echocardio-grams video generation
Yiwei Li, Sekeun Kim, Zihao Wu +8
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clin…
EchoFM: Foundation Model for Generalizable Echocardiogram Analysis
Sekeun Kim, Pengfei Jin, Sifan Song +6
Foundation models have recently gained significant attention because of their generalizability and adaptability across multiple tasks and data distributions. Although medical found…
Beyond Adapter Retrieval: Latent Geometry-Preserving Composition via Sparse Task Projection
Pengfei Jin, Peng Shu, Sifan Song +6
Recent advances in parameter-efficient transfer learning have demonstrated the utility of composing LoRA adapters from libraries of pretrained modules. However, most existing appro…
Biomedical SAM 2: Segment Anything in Biomedical Images and Videos
Zhiling Yan, Weixiang Sun, Rong Zhou +8
Medical image segmentation and video object segmentation are essential for diagnosing and analyzing diseases by identifying and measuring biological structures. Recent advances in…
Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction
Qing Xiao, Siyeop Yoon, Hui Ren +6
Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals, with changes in cortical thickness (CTh) closely linked to i…
Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis
Shaochen Xu, Zihao Wu, Huaqin Zhao +7
In this study, we leverage LLM to enhance the semantic analysis and develop similarity metrics for texts, addressing the limitations of traditional unsupervised NLP metrics like RO…
Bi-VLGM : Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation
Chen Wenting, Liu Jie, Yuan Yixuan
Medical reports with substantial information can be naturally complementary to medical images for computer vision tasks, and the modality gap between vision and language can be sol…