1 citations · 1 across the 7 of their papers we have counts for
10 papers
Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models
Ruiyang Li, Fang Liu, Licheng Jiao +8
Medical image segmentation supports clinical workflows by precisely delineating anatomical structures and lesions. However, medical image datasets medical image datasets suffer fro…
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…
VIVID-Med: LLM-Supervised Structured Pretraining for Deployable Medical ViTs
Xiyao Wang, Xiaoyu Tan, Yang Dai +3
Vision-language pretraining has driven significant progress in medical image analysis. However, current methods typically supervise visual encoders using one-hot labels or free-for…
Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos
Jieyun Bai, Zihao Zhou, Yitong Tang +60
A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income c…
EEG-VLM: A Hierarchical Vision-Language Model with Multi-Level Feature Alignment and Visually Enhanced Language-Guided Reasoning for EEG Image-Based Sleep Stage Prediction
Xihe Qiu, Gengchen Ma, Haoyu Wang +3
Sleep stage classification based on electroencephalography (EEG) is fundamental for assessing sleep quality and diagnosing sleep-related disorders. However, most traditional machin…
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…
Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation
Ruiyi Li, Yuting He, Rongjun Ge +4
Domain adaptation (DA) for cardiac ultrasound image segmentation is clinically significant and valuable. However, previous domain adaptation methods are prone to be affected by the…
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation
Qingtao Pan, Wenhao Qiao, Jingjiao Lou +2
Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. However, it…
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…
TASL-Net: Tri-Attention Selective Learning Network for Intelligent Diagnosis of Bimodal Ultrasound Video
Chengqian Zhao, Zhao Yao, Zhaoyu Hu +8
In the intelligent diagnosis of bimodal (gray-scale and contrast-enhanced) ultrasound videos, medical domain knowledge such as the way sonographers browse videos, the particular ar…