Analysis of histopathology images : from traditional machine learning to deep learning

Jimenez-del-Toro, Oscar (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland) ; Otálora, Sebastian (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Andersson, Mats (ContextVision, Stockholm, Sweden) ; Eurén, Kristian (ContextVision, Stockholm, Sweden) ; Hedlund, Martin (ContextVision, Stockholm, Sweden) ; Rousson, Mikael (ContextVision, Stockholm, Sweden) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland) ; Atzori, Manfredo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

Digitizing pathology is a current trend that makes large amounts of visual data available for automatic analysis. It allows to visualize and interpret pathologic cell and tissue samples in high-resolution images and with the help of computer tools. This opens the possibility to develop image analysis methods that help pathologists and support their image descriptions (i.e., staging, grading) with objective quantification of image features. Numerous detection, classification and segmentation algorithms of the underlying tissue primitives in histopathology images have been proposed in this respect. To better select the most suitable algorithms for histopathology tasks, biomedical image analysis challenges have evaluated and compared both traditional feature extraction with machine learning and deep learning techniques. This chapter provides an overview of methods addressing the analysis of histopathology images, as well as a brief description of the tasks they aim to solve. It is focused on histopathology images containing textured areas of different types.


Mots-clés:
Faculté:
Economie et Services
Ecole:
HEG-VS
Institut:
Institut Informatique de gestion
Classification:
Informatique
Adresse bibliogr.:
[S. l.], Elsevier
Date:
[S. l.]
Elsevier
2017
Pagination:
pp. 281–314
Publié dans
Biomedical texture analysis : fundamentals, tools and challenges
DOI:
ISBN:
978-0-12-812133-7
Ressource(s) externe(s):
Le document apparaît dans:

Note: The status of this file is: restricted


 Notice créée le 2017-12-18, modifiée le 2018-12-11

Fichiers:
Télécharger le document
PDF

Évaluer ce document:

Rate this document:
1
2
3
 
(Pas encore évalué)