Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans

Silas Nyboe Ørting*, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M.W. Wille, Marleen de Bruijne

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

Abstract

Supervised feature learning using convolutional neural networks (CNNs) can provide concise and disease relevant representations of medical images. However, training CNNs requires annotated image data. Annotating medical images can be a time-consuming task and even expert annotations are subject to substantial inter- and intra-rater variability. Assessing visual similarity of images instead of indicating specific pathologies or estimating disease severity could allow non-experts to participate, help uncover new patterns, and possibly reduce rater variability. We consider the task of assessing emphysema extent in chest CT scans. We derive visual similarity triplets from visually assessed emphysema extent and learn a low dimensional embedding using CNNs. We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets. To our knowledge this is the first medical image application where similarity triplets has been used to learn a feature representation that can be used for embedding unseen test images.

Original languageEnglish
Title of host publicationIntravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018
EditorsSu-Lin Lee, Emanuele Trucco, Lena Maier-Hein, Stefano Moriconi, Shadi Albarqouni, Pierre Jannin, Simone Balocco, Guillaume Zahnd, Diana Mateus, Zeike Taylor, Stefanie Demirci, Danail Stoyanov, Raphael Sznitman, Anne Martel, Veronika Cheplygina, Eric Granger, Luc Duong
Pages140-149
Number of pages10
DOIs
Publication statusPublished - 2018
Event7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sept 201816 Sept 2018

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11043 LNCS
ISSN0302-9743

Conference

Conference7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period16/09/1816/09/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

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