7 citations · 13 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…