8 papers
Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentation
Rongjun Ge, Dongyang Wang, Heng Zhu +3
Interactive egocentric medical image segmentation (IEMIS) plays an important role in smart-glasses-assisted medical image review, segmenting the medical targets a clinician refers…
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…
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…
MRI Contrast Enhancement Kinetics World Model
Jindi Kong, Yuting He, Cong Xia +2
Clinical MRI contrast acquisition suffers from inefficient information yield, which presents as a mismatch between the risky and costly acquisition protocol and the fixed and spars…
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
Yuting He, Shuo Li
Contrastive learning (CL) has become a cornerstone of self-supervised pretraining (SSP) in foundation models, however, extending CL to pixel-wise representation, crucial for medica…