A self-supervised contrastive learning approach for whole slide image representation in digital pathology

Image analysis in digital pathology has proven to be one of the most challenging fields in medical imaging for AI-driven classification and search tasks. Due to their gigapixel dimensions, whole slide images (WSIs) are difficult to represent for computational pathology. Self-supervised learning (SSL...

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Detalles Bibliográficos
Autores principales: Fashi, Parsa Ashrafi, Hemati, Sobhan, Babaie, Morteza, Gonzalez, Ricardo, Tizhoosh, H.R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808093/
https://www.ncbi.nlm.nih.gov/pubmed/36605114
http://dx.doi.org/10.1016/j.jpi.2022.100133

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808093/
https://www.ncbi.nlm.nih.gov/pubmed/36605114
http://dx.doi.org/10.1016/j.jpi.2022.100133

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