5 papers
RelativeFlow: Taming Medical Image Denoising Learning with Noisy Reference
Yuxin Liu, Yiqing Dong, Wenxue Yu +4
Medical image denoising (MID) lacks absolutely clean images for supervision, leading to a noisy reference problem that fundamentally limits denoising performance. Existing simulate…
Human Gaze-based Dual Teacher Guidance Learning for Semi-Supervised Medical Image Segmentation
Rongjun Ge, Chong Wang, Yuxin Liu +10
In the field of medical image segmentation, the scarcity of labeled data poses a major challenge for existing models to accurately perceive target regions. Compared with manual ann…
Generative Data-engine Foundation Model for Universal Few-shot 2D Vascular Image Segmentation
Rongjun Ge, Xin Li, Yuxing Liu +8
The segmentation of 2D vascular structures via deep learning holds significant clinical value but is hindered by the scarcity of annotated data, severely limiting its widespread ap…
CG-DMER: Hybrid Contrastive-Generative Framework for Disentangled Multimodal ECG Representation Learning
Ziwei Niu, Hao Sun, Shujun Bian +4
Accurate interpretation of electrocardiogram (ECG) signals is crucial for diagnosing cardiovascular diseases. Recent multimodal approaches that integrate ECGs with accompanying cli…
Imaging foundation model for universal enhancement of non-ideal measurement CT
Rongjun Ge, Yuxin Liu, Zhan Wu +7
Non-ideal measurement computed tomography (NICT) employs suboptimal imaging protocols to expand CT applications. However, the resulting trade-offs degrade image quality, limiting c…