Image magnification regression using DenseNet for exploiting histopathology open access content

Otalora, Sebastian (University of Geneva (UNIGE), Geneva, Switzerland ; University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Atzori, Manfredo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Andrearczyk, Vincent (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Müller, Henning (University of Geneva (UNIGE), Geneva, Switzerland ; University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

Open access medical content databases such as PubMed Central and TCGA offer possibilities to obtain large amounts of images for training deep learning models. Nevertheless, accurate labeling of large-scale medical datasets is not available and poses challenging tasks for using such datasets. Predicting unknown magnification levels and standardize staining procedures is a necessary preprocessing step for using this data in retrieval and classification tasks. In this paper, a CNN-based regression approach to learn the magnification of histopathology images is presented, comparing two deep learning architectures tailored to regress the magnification. A comparison of the performance of the models is done in a dataset of 34,441 breast cancer patches with several magnifications. The best model, a fusion of DenseNet-based CNNs, obtained a kappa score of 0.888. The methods are also evaluated qualitatively on a set of images from biomedical journals and TCGA prostate patches.


Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Economie/gestion
Publisher:
Cham, Springer
Date:
2018-09
Cham
Springer
Pagination:
pp. 148-155
Published in:
Computational pathology and ophthalmic medical image analysis
Author of the book:
Stoyanov, Danail ; ed. ; University College London, UK
DOI:
ISBN:
978-3-030-00948-9
External resources:
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2018-11-14, last modified 2019-03-01

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)