4 citations · 5 across the 7 of their papers we have counts for
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eess.IV2024★ 4 cited
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…
eess.IV2024★ 1 cited
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…
cs.CV2024
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…