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Deep learning in histopathology: the path to the clinic

Authors :
Laak, J.A.W.M. van der
Litjens, G.J.S.
Ciompi, F.
Laak, J.A.W.M. van der
Litjens, G.J.S.
Ciompi, F.
Source :
Nature Medicine; 775; 784; 1078-8956; 5; 27; ~Nature Medicine~775~784~~~1078-8956~5~27~~
Publication Year :
2021

Abstract

Contains fulltext : 235736.pdf (Publisher’s version ) (Open Access)<br />Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques. This Review provides an overview of the current state of the field, as well as describing the challenges that still need to be addressed before artificial intelligence in histopathology can achieve clinical value.

Details

Database :
OAIster
Journal :
Nature Medicine; 775; 784; 1078-8956; 5; 27; ~Nature Medicine~775~784~~~1078-8956~5~27~~
Publication Type :
Electronic Resource
Accession number :
edsoai.on1284088977
Document Type :
Electronic Resource