Cargando…
Issues in Melanoma Detection: Semisupervised Deep Learning Algorithm Development via a Combination of Human and Artificial Intelligence
BACKGROUND: Automatic skin lesion recognition has shown to be effective in increasing access to reliable dermatology evaluation; however, most existing algorithms rely solely on images. Many diagnostic rules, including the 3-point checklist, are not considered by artificial intelligence algorithms,...
Autores principales: | Zhang, Xinyuan, Xie, Ziqian, Xiang, Yang, Baig, Imran, Kozman, Mena, Stender, Carly, Giancardo, Luca, Tao, Cui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10334941/ https://www.ncbi.nlm.nih.gov/pubmed/37632881 http://dx.doi.org/10.2196/39113 |
Ejemplares similares
-
Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
por: Li, Qun, et al.
Publicado: (2022) -
Retracted: Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
por: Intelligence and Neuroscience, Computational
Publicado: (2023) -
A Semisupervised Support Vector Machines Algorithm for BCI Systems
por: Qin, Jianzhao, et al.
Publicado: (2007) -
Semisupervised Deep State-Space Model for Plant Growth Modeling
por: Shibata, S., et al.
Publicado: (2020) -
An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer
por: Providência, Laura, et al.
Publicado: (2021)