12 papers
Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning
Siyu Liu, Guangqi Wen, Peng Cao +4
Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of functional pathways. However, learning the…
Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation
Jianqiang Lin, Zhiqiang Shen, Peng Cao +3
Although diffusion models have achieved remarkable progress in multi-modal magnetic resonance imaging (MRI) translation tasks, existing methods still tend to suffer from anatomical…
IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis
Chongxiao Wang, Junjie Liang, Peng Cao +2
Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression detection aims to improve diagn…
BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
Xiaolong Li, Guiliang Guo, Guangqi Wen +6
Mental disorder populations exhibit pronounced heterogeneity -- that is, the significant differences between samples -- poses a significant challenge to the definition of positive…
Visually-Guided Controllable Medical Image Generation via Fine-Grained Semantic Disentanglement
Xin Huang, Junjie Liang, Qingshan Hou +4
Medical image synthesis is crucial for alleviating data scarcity and privacy constraints. However, fine-tuning general text-to-image (T2I) models remains challenging, mainly due to…
BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling
Guiliang Guo, Guangqi Wen, Lingwen Liu +6
Dynamic functional connectivity captures time-varying brain states for better neuropsychiatric diagnosis and spatio-temporal interpretability, i.e., identifying when discriminative…
Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis
Jingfeng Tang, Peng Cao, Guangqi Wen +3
Brain network analysis based on functional Magnetic Resonance Imaging (fMRI) is pivotal for diagnosing brain disorders. Existing approaches typically rely on predefined functional…
SynMatch: Rethinking Consistency in Medical Image Segmentation with Sparse Annotations
Zhiqiang Shen, Peng Cao, Xiaoli Liu +2
Label scarcity remains a major challenge in deep learning-based medical image segmentation. Recent studies use strong-weak pseudo supervision to leverage unlabeled data. However, p…
ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Xi Chen, Zhiqiang Shen, Peng Cao +2
Medical images are usually collected from multiple domains, leading to domain shifts that impair the performance of medical image segmentation models. Domain Generalization (DG) ai…
FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation
Qingshan Hou, Meng Wang, Peng Cao +4
Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training…
Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation
Zhiqiang Shen, Peng Cao, Jinzhu Yang +2
Due to domain shifts across diverse medical imaging modalities, learned segmentation models often suffer significant performance degradation during deployment. We posit that these…
SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning
Chunzhen Jin, Yongfeng Huang, Yaqi Wang +2
Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources…