PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Sidulova, M.* ; Sun, X. ; Gossmann, A.*

Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set.

In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2023). Berlin [u.a.]: Springer, 2023. 666-675 (Lect. Notes Comput. Sc. ; 14227 LNCS)
DOI
Consideration of subgroups or domains within medical image datasets is crucial for the development and evaluation of robust and generalizable machine learning systems. To tackle the domain identification problem, we examine deep unsupervised generative clustering approaches for representation learning and clustering. The Variational Deep Embedding (VaDE) model is trained to learn lower-dimensional representations of images based on a Mixture-of-Gaussians latent space prior distribution while optimizing cluster assignments. We propose the Conditionally Decoded Variational Deep Embedding (CDVaDE) model which incorporates additional variables of choice, such as the class labels, as conditioning factors to guide the clustering towards subgroup structures in the data which have not been known or recognized previously. We analyze the behavior of CDVaDE on multiple datasets and compare it to other deep clustering algorithms. Our experimental results demonstrate that the considered models are capable of separating digital pathology images into meaningful subgroups. We provide a general-purpose implementation of all considered deep clustering methods as part of the open source Python package DomId (https://github.com/DIDSR/DomId ).
Altmetric
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Deep Clustering ; Domain Identification ; Generative Model ; Subgroup Identification ; Variational Autoencoder
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14227 LNCS, Heft: , Seiten: 666-675 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540007-001
DOI 10.1007/978-3-031-43993-3_64
Scopus ID 85174727014
Erfassungsdatum 2023-11-28
[➜Korrektur melden]