ArticleiScience2024
CTH-Net: A CNN and Transformer hybrid network for skin lesion segmentation.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Local global feature enhanced transformer with attention pruning for precise medical image segmentation.iScience · 2026Article
- Multi-chaotic signal identification employing a causal cross-correlation neural network.iScience · 2026Article
- MSF-VMDNet for multi class segmentation of skin cancer whole slide images using a multi frequency dual encoder network.Scientific reports · 2026Article
- Multi-view hybrid encoder U-Net for 3D renal vascular medical image segmentation.Scientific reports · 2025Article
- StarMA Net: A star-shape multi-scale attention network for medical imaging classification.iScience · 2025Article
- A mixed Mamba U-net for prostate segmentation in MR images.Scientific reports · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
Automatically and accurately segmenting skin lesions can be challenging, due to factors such as low contrast and fuzzy boundaries. This paper proposes a hybrid encoder-decoder model (CTH-Net) based on convolutional neural network (CNN) and Transformer, capitalizing on the advantages of these approaches. We propose three modules for skin lesion segmentation and seamlessly connect them with carefully designed model architecture. Better segmentation performance is achieved by introducing SoftPool in the CNN branch and sandglass block in the bottleneck layer. Extensive experiments were conducted on four publicly accessible skin lesion datasets, ISIC 2016, ISIC 2017, ISIC 2018, and PH
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Registered trials
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