AID-U-Net: An Innovative Deep Convolutional Architecture for Semantic Segmentation of Biomedical Images

Semantic segmentation of biomedical images found its niche in screening and diagnostic applications. Recent methods based on deep learning convolutional neural networks have been very effective, since they are readily adaptive to biomedical applications and outperform other competitive segmentation...

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Detalles Bibliográficos
Autores principales: Tashk, Ashkan, Herp, Jürgen, Bjørsum-Meyer, Thomas, Koulaouzidis, Anastasios, Nadimi, Esmaeil S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777521/
https://www.ncbi.nlm.nih.gov/pubmed/36552959
http://dx.doi.org/10.3390/diagnostics12122952

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777521/
https://www.ncbi.nlm.nih.gov/pubmed/36552959
http://dx.doi.org/10.3390/diagnostics12122952

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