7 citations · 19 across the 18 of their papers we have counts for
18 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…
Research on Medical Named Entity Identification Based On Prompt-Biomrc Model and Its Application in Intelligent Consultation System
Jinzhu Yang
This study is dedicated to exploring the application of prompt learning methods to advance Named Entity Recognition (NER) within the medical domain. In recent years, the emergence…
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…
Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation
Junming Su, Zhiqiang Shen, Peng Cao +2
The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate…
Adaptive Mix for Semi-Supervised Medical Image Segmentation
Zhiqiang Shen, Peng Cao, Junming Su +2
Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, blending two or more images to generate strong-perturbed samples for strong-weak ps…
Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective
Zhiqiang Shen, Peng Cao, Junming Su +2
This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-a…
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images
Qingshan Hou, Shuai Cheng, Peng Cao +4
Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and…
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Haonan Wang, Peng Cao, Xiaoli Liu +2
Most state-of-the-art methods for medical image segmentation adopt the encoder-decoder architecture. However, this U-shaped framework still has limitations in capturing the non-loc…
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement
Qingshan Hou, Peng Cao, Jiaqi Wang +3
Retinal fundus images have been applied for the diagnosis and screening of eye diseases, such as Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). However, both low-qualit…
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Zhiqiang Shen, Peng Cao, Hua Yang +3
Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models te…