Whole Slide Image Quality in Digital Pathology: Review and Perspectives-Reference-Cited by-同舟云学术

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Published:2022 Issue: Volume:10 Page:131005-131035
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Brixtel Romain¹^ORCID,Bougleux Sebastien²^ORCID,Lezoray Olivier²^ORCID,Caillot Yann¹^ORCID,Lemoine Benoit¹^ORCID,Fontaine Mathieu¹^ORCID,Nebati Dalal¹^ORCID,Renouf Arnaud¹^ORCID

Affiliation:

1. Datexim, Caen, France

2. UNICAEN, ENSICAEN, CNRS, GREYC, Normandie Université, Caen, France

Funder

Région Normandie and BPI France through a PTZI for Datexim’s Research and Development Team

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09973240.pdf?arnumber=9973240

Reference226 articles.

1. Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers

2. Quality control of whole-slide images through multi-class semantic segmentation of artifacts;smit;Proc MIDL,2021

3. Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology

4. The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

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