A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
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A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. / Wickstrøm, Kristoffer Knutsen; Østmo, Eirik Agnalt; Radiya, Keyur; Mikalsen, Karl Øyvind; Kampffmeyer, Michael Christian; Jenssen, Robert.
I: Computerized Medical Imaging and Graphics, Bind 107, 102239, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
AU - Wickstrøm, Kristoffer Knutsen
AU - Østmo, Eirik Agnalt
AU - Radiya, Keyur
AU - Mikalsen, Karl Øyvind
AU - Kampffmeyer, Michael Christian
AU - Jenssen, Robert
N1 - Publisher Copyright: © 2023 The Author(s)
PY - 2023
Y1 - 2023
N2 - Deep learning-based approaches for content-based image retrieval (CBIR) of computed tomography (CT) liver images is an active field of research, but suffer from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the trustworthiness of deep CBIR systems. We address these limitations by: (1) Proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure, and, (2) by providing the first representation learning explainability analysis in the context of CBIR of CT liver images. Results demonstrate improved performance compared to the standard self-supervised approach across several metrics, as well as improved generalization across datasets. Further, we conduct the first representation learning explainability analysis in the context of CBIR, which reveals new insights into the feature extraction process. Lastly, we perform a case study with cross-examination CBIR that demonstrates the usability of our proposed framework. We believe that our proposed framework could play a vital role in creating trustworthy deep CBIR systems that can successfully take advantage of unlabeled data.
AB - Deep learning-based approaches for content-based image retrieval (CBIR) of computed tomography (CT) liver images is an active field of research, but suffer from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the trustworthiness of deep CBIR systems. We address these limitations by: (1) Proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure, and, (2) by providing the first representation learning explainability analysis in the context of CBIR of CT liver images. Results demonstrate improved performance compared to the standard self-supervised approach across several metrics, as well as improved generalization across datasets. Further, we conduct the first representation learning explainability analysis in the context of CBIR, which reveals new insights into the feature extraction process. Lastly, we perform a case study with cross-examination CBIR that demonstrates the usability of our proposed framework. We believe that our proposed framework could play a vital role in creating trustworthy deep CBIR systems that can successfully take advantage of unlabeled data.
KW - Content-based image retrieval
KW - CT liver imaging
KW - Explainability
KW - Self-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85159367596&partnerID=8YFLogxK
U2 - 10.1016/j.compmedimag.2023.102239
DO - 10.1016/j.compmedimag.2023.102239
M3 - Journal article
C2 - 37207397
AN - SCOPUS:85159367596
VL - 107
JO - Computerized Medical Imaging and Graphics
JF - Computerized Medical Imaging and Graphics
SN - 0895-6111
M1 - 102239
ER -
ID: 347977905