Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

Abstract The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not s...

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Main Authors: Niccolò Marini, Stefano Marchesin, Sebastian Otálora, Marek Wodzinski, Alessandro Caputo, Mart van Rijthoven, Witali Aswolinskiy, John-Melle Bokhorst, Damian Podareanu, Edyta Petters, Svetla Boytcheva, Genziana Buttafuoco, Simona Vatrano, Filippo Fraggetta, Jeroen van der Laak, Maristella Agosti, Francesco Ciompi, Gianmaria Silvello, Henning Muller, Manfredo Atzori
Format: Article
Language:English
Published: Nature Portfolio 2022-07-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-022-00635-4

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https://doi.org/10.1038/s41746-022-00635-4

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