Cargando…
HMT-Net: Transformer and MLP Hybrid Encoder for Skin Disease Segmentation
At present, convolutional neural networks (CNNs) have been widely applied to the task of skin disease image segmentation due to the fact of their powerful information discrimination abilities and have achieved good results. However, it is difficult for CNNs to capture the connection between long-ran...
Autores principales: | Yang, Sen, Wang, Liejun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10051843/ https://www.ncbi.nlm.nih.gov/pubmed/36991777 http://dx.doi.org/10.3390/s23063067 |
Ejemplares similares
-
HEA-Net: Attention and MLP Hybrid Encoder Architecture for Medical Image Segmentation
por: An, Lijing, et al.
Publicado: (2022) -
MLP-based classification of COVID-19 and skin diseases
por: Zhang, Ruize, et al.
Publicado: (2023) -
HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation
por: Hu, Xiaolong, et al.
Publicado: (2022) -
Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation
por: Cheng, Zhikun, et al.
Publicado: (2023) -
TC-Net: Dual coding network of Transformer and CNN for skin lesion segmentation
por: Dong, Yuying, et al.
Publicado: (2022)