Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification

Abstract Background One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated data include: using transfer learning, data...

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Bibliographic Details
Main Authors: Sebastian Otálora, Niccolò Marini, Henning Müller, Manfredo Atzori
Format: Article
Language:English
Published: BMC 2021-05-01
Series:BMC Medical Imaging
Subjects:
Computational pathology
Weak supervision
Transfer learning
Prostate cancer
Deep learning
Online Access:https://doi.org/10.1186/s12880-021-00609-0

Internet

https://doi.org/10.1186/s12880-021-00609-0

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