4 citations · 5 across the 7 of their papers we have counts for
8 papers
CFCH: Coarse-Fine Collaborative Hierarchical Learning for Anterior Segment Disease Analysis
Peng Wang, Haohan Zou, Yanlin Wu +3
Accurate classification of anterior segment diseases is crucial for ophthalmic screening and diagnosis. However, slit-lamp image analysis remains challenging due to substantial var…
LM-PCVMNet: Pediatric Cervical Vertebral Maturation Analysis with Deep Fusion of Landmarks and Metadata
Peng Wang, Wanzhen Song, Anli Wang +3
Cervical vertebral maturation (CVM) assessment plays a pivotal role in orthodontic diagnosis and determining the optimal timing of treatment, especially for pediatric patients. In…
Med-URWKV†: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation
Zhenhuan Zhou, Yining Li, Yanlin Wu +3
Medical image segmentation is a fundamental task in computer-aided diagnosis and treatment. Existing approaches based on CNNs, ViTs, Mamba, and hybrid models still suffer from limi…
Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation
Zhenhuan Zhou, Yuchen Zhang, Along He +3
Root canal (RC) treatment is a highly delicate and technically complex procedure in clinical practice, heavily influenced by the clinicians' experience and subjective judgment. Dee…
PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development
Zhenhuan Zhou, Yuchen Zhang, Ruihong Xu +2
Deep learning (DL), a pivotal technology in artificial intelligence, has recently gained substantial traction in the domain of dental auxiliary diagnosis. However, its application…
TransUKAN:Computing-Efficient Hybrid KAN-Transformer for Enhanced Medical Image Segmentation
Yanlin Wu, Tao Li, Zhihong Wang +2
U-Net is currently the most widely used architecture for medical image segmentation. Benefiting from its unique encoder-decoder architecture and skip connections, it can effectivel…
Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation
Zhenhuan Zhou, Along He, Yanlin Wu +3
In medical images, various types of lesions often manifest significant differences in their shape and texture. Accurate medical image segmentation demands deep learning models with…
FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation
Along He, Tao Li, Yanlin Wu +2
Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-su…