Combining unsupervised feature learning and riesz wavelets for histopathology image representation: application to identifying anaplastic medulloblastoma

Atzori, Manfredo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Muller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; et al.

Medulloblastoma (MB) is a type of brain cancer that represent roughly 25% of all brain tumors in children. In the anaplastic medulloblastoma subtype, it is important to identify the degree of irregularity and lack of organizations of cells as this correlates to disease aggressiveness and is of clinical value when evaluating patient prognosis. This paper presents an image representation to distinguish these subtypes in histopathology slides. The approach combines learned features from (i) an unsupervised feature learning method using topographic independent component analysis that captures scale, color and translation invariances, and (ii) learned linear combinations of Riesz wavelets calculated at several orders and scales capturing the granularity of multiscale rotation-covariant information. The contribution of this work is to show that the combination of two complementary approaches for feature learning (unsupervised and supervised) improves the classication performance. Our approach outperforms the best methods in literature with statistical signicance, achieving 99% accuracy over region-based data comprising 7,500 square regions from 10 patient studies diagnosed with medulloblastoma (5 anaplastic and 5 non-anaplastic).


Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Cham, Springer International Publishing
Date:
Cham
Springer International Publishing
2015
Pagination:
pp. 581-588
Published in:
Medical image computing and computer-assisted intervention – MICCAI 2015
Series Statement:
Lecture Notes in Computer Science, vol. 9349
DOI:
ISBN:
978-3-319-24552-2
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2016-03-23, last modified 2018-12-20

Fulltext:
Download fulltext
PDF

Rate this document:

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