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A novel collaborative self-supervised learning method for radiomic data
The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative se...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440826/ https://www.ncbi.nlm.nih.gov/pubmed/37321358 http://dx.doi.org/10.1016/j.neuroimage.2023.120229 |