Zobrazeno 1 - 4
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pro vyhledávání: '"Simon M. Thomas"'
Publikováno v:
Data in Brief, Vol 39, Iss , Pp 107587- (2021)
Densely labelled segmentation data for digital pathology images is costly to produce but is invaluable to training effective machine learning models. We make available 290 hand-annotated histopathology tissue sections of the 3 most common skin cancer
Externí odkaz:
https://doaj.org/article/f31868d8272840e993f554c05907ffb6
Autor:
Simon M. Thomas, Mahmoud R. Fassad, Mitali P. Patel, Isabelle Honoré, Jean-François Papon, Estelle Escudier, Gunnar E. Carlsson, Robert Wilson, Siobhán B. Carr, Camille Parsons, I.C.M. Bon, Sunayna Best, Amelia Shoemark, Matthew S. Edwards, Deborah J. Morris-Rosendahl, Joost Brandsma, Bruna Rubbo, Bernard Maitre, Eric G. Haarman, Gregory Jouvion, Guillaume Thouvenin, David Hunt, Woolf T. Walker, Pierre-Régis Burgel, Miles W. Carroll, Marie Legendre, Michael R. Loebinger, Hannah M. Mitchison, C. Hogg, Jane S. Lucas
Publikováno v:
C65. COMPUTATIONAL METHODS, MODELS, AND DRUG DELIVERY.
Publikováno v:
Data in Brief
Data in Brief, Vol 39, Iss, Pp 107587-(2021)
Data in Brief, Vol 39, Iss, Pp 107587-(2021)
Densely labelled segmentation data for digital pathology images is costly to produce but is invaluable to training effective machine learning models. We make available 290 hand-annotated histopathology tissue sections of the 3 most common skin cancer
Publikováno v:
Medical Image Analysis. 68:101915
We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constit