Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform

We demonstrate that isomorphically mapping gray-level medical image matrices onto energy spaces underlying the framework of fast data density functional transform (fDDFT) can achieve the unsupervised recognition of lesion morphology. By introducing the architecture of geometric deep learning and met...

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Detalles Bibliográficos
Autores principales: Huang, Shin-Jhe, Chen, Chien-Chang, Kao, Yamin, Lu, Henry Horng-Shing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442428/
https://www.ncbi.nlm.nih.gov/pubmed/37604860
http://dx.doi.org/10.1038/s41598-023-40848-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442428/
https://www.ncbi.nlm.nih.gov/pubmed/37604860
http://dx.doi.org/10.1038/s41598-023-40848-5

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