Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning

We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and...

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Detalles Bibliográficos
Autores principales: Dominic, Jeffrey, Bhaskhar, Nandita, Desai, Arjun D., Schmidt, Andrew, Rubin, Elka, Gunel, Beliz, Gold, Garry E., Hargreaves, Brian A., Lenchik, Leon, Boutin, Robert, Chaudhari, Akshay S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951871/
https://www.ncbi.nlm.nih.gov/pubmed/36829701
http://dx.doi.org/10.3390/bioengineering10020207

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951871/
https://www.ncbi.nlm.nih.gov/pubmed/36829701
http://dx.doi.org/10.3390/bioengineering10020207

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