5 citations · 8 across the 24 of their papers we have counts for
26 papers
UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
Yunzhou Li, Jiesi Hu, Yanwu Yang +5
Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fragmented by prompt paradigms and…
Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis
Hairui Chen, Yanwu Yang, Jianfeng Cao +3
Brain networks exhibit a modular community structure that varies across individuals and neurological conditions. However, existing self-supervised learning (SSL) methods often over…
Multi-Stream LLMs: Unblocking Language Models with Parallel Streams of Thoughts, Inputs and Outputs
Guinan Su, Yanwu Yang, Xueyan Li +1
The continued improvements in language model capability have unlocked their widespread use as drivers of autonomous agents, for example in coding or computer use applications. Howe…
Learning beyond Site Bias for OOD Generalization in Brain Networks
Yingxu Wang, Kunyu Zhang, Yanwu Yang +5
Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing methods often degrade under cross-si…
PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Kunyu Zhang, Yanwu Yang, Jing Zhang +2
Recent deep learning methods for fMRI-based diagnosis have achieved promising accuracy by modeling functional connectivity networks. However, standard approaches often struggle wit…
HippMetric: A skeletal-representation-based framework for cross-sectional and longitudinal hippocampal substructural morphometry
Na Gao, Chenfei Ye, Yanwu Yang +7
Accurate characterization of hippocampal substructure is crucial for detecting subtle structural changes and identifying early neurodegenerative biomarkers. However, high inter-sub…
Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models
Guinan Su, Yanwu Yang, Li Shen +3
Mixture-of-Experts (MoE) models achieve efficient scaling through sparse expert activation, but often suffer from suboptimal routing decisions due to distribution shifts in deploym…
Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning
Jiesi Hu, Yanwu Yang, Zhiyu Ye +4
Universal models for medical image segmentation, such as interactive and in-context learning (ICL) models, offer strong generalization but require extensive annotations. Interactiv…
Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis
Jiesi Hu, Yanwu Yang, Zhiyu Ye +4
The rise of In-Context Learning (ICL) for universal medical image segmentation has introduced an unprecedented demand for large-scale, diverse datasets for training, exacerbating t…
Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancement
Jiesi Hu, Jianfeng Cao, Yanwu Yang +4
In-context learning (ICL) offers a promising paradigm for universal medical image analysis, enabling models to perform diverse image processing tasks without retraining. However, c…
MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation
Yanwu Yang, Guinan Su, Jiesi Hu +3
Universal medical image segmentation models have emerged as a promising paradigm due to their strong generalizability across diverse tasks, showing great potential for a wide range…
Hierarchical Characterization of Brain Dynamics via State Space-based Vector Quantization
Yanwu Yang, Thomas Wolfers
Understanding brain dynamics through functional Magnetic Resonance Imaging (fMRI) remains a fundamental challenge in neuroscience, particularly in capturing how the brain transitio…
GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching
Guinan Su, Li Shen, Lu Yin +3
Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. However, such impressive capability typically comes with a substantial mod…
Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D
Jiesi Hu, Chenfei Ye, Yanwu Yang +5
In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance f…
Advancing Brain Imaging Analysis Step-by-step via Progressive Self-paced Learning
Yanwu Yang, Hairui Chen, Jiesi Hu +2
Recent advancements in deep learning have shifted the development of brain imaging analysis. However, several challenges remain, such as heterogeneity, individual variations, and t…
Centerline Boundary Dice Loss for Vascular Segmentation
Pengcheng Shi, Jiesi Hu, Yanwu Yang +3
Vascular segmentation in medical imaging plays a crucial role in analysing morphological and functional assessments. Traditional methods, like the centerline Dice (clDice) loss, en…
BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning
Yanwu Yang, Chenfei Ye, Guinan Su +6
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-col…
DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animation
Guinan Su, Yanwu Yang, Zhifeng Li
In recent years, audio-driven 3D facial animation has gained significant attention, particularly in applications such as virtual reality, gaming, and video conferencing. However, a…
Prompt Your Mind: Refine Personalized Text Prompts within Your Mind
Guinan Su, Yanwu Yang, Jie Guo
Large language models (LLMs) have demonstrated remarkable potential in natural language understanding and generation, making them valuable tools for enhancing conversational intera…
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Jiesi Hu, Yanwu Yang, Xutao Guo +2
Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data. In medical imaging sce…
NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation
Pengcheng Shi, Xutao Guo, Yanwu Yang +2
Convolutional neural networks (CNN) and Transformer variants have emerged as the leading medical image segmentation backbones. Nonetheless, due to their limitations in either prese…
DECOR-NET: A COVID-19 Lung Infection Segmentation Network Improved by Emphasizing Low-level Features and Decorrelating Features
Jiesi Hu, Yanwu Yang, Xutao Guo +1
Since 2019, coronavirus Disease 2019 (COVID-19) has been widely spread and posed a serious threat to public health. Chest Computed Tomography (CT) holds great potential for screeni…
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation
Xutao Guo, Yanwu Yang, Chenfei Ye +3
Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise…
Multi-modal Dynamic Graph Network: Coupling Structural and Functional Connectome for Disease Diagnosis and Classification
Yanwu Yang, Xutao Guo, Zhikai Chang +3
Multi-modal neuroimaging technology has greatlly facilitated the efficiency and diagnosis accuracy, which provides complementary information in discovering objective disease biomar…