Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans...

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Detalles Bibliográficos
Autores principales: Bounias, Dimitrios, Singh, Ashish, Bakas, Spyridon, Pati, Sarthak, Rathore, Saima, Akbari, Hamed, Bilello, Michel, Greenberger, Benjamin A., Lombardo, Joseph, Chitalia, Rhea D., Jahani, Nariman, Gastounioti, Aimilia, Hershman, Michelle, Roshkovan, Leonid, Katz, Sharyn I., Yousefi, Bardia, Lou, Carolyn, Simpson, Amber L., Do, Richard K. G., Shinohara, Russell T., Kontos, Despina, Nikita, Konstantina, Davatzikos, Christos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494410/
https://www.ncbi.nlm.nih.gov/pubmed/34621541
http://dx.doi.org/10.3390/app11167488

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494410/
https://www.ncbi.nlm.nih.gov/pubmed/34621541
http://dx.doi.org/10.3390/app11167488

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