Multi-modal Volumetric Concept Activation to Explain Detection and Classification of Metastatic Prostate Cancer on PSMA-PET/CT
Kraaijveld, R. C.J.; Philippens, M. E.P.; Eppinga, W. S.C.; Jürgenliemk-Schulz, I. M.; Gilhuijs, K. G.A.; Kroon, P. S.; van der Velden, B. H.M.
(2022)
Interpretability of Machine Intelligence in Medical Image Computing - 5th International Workshop, iMIMIC 2022, Held in Conjunction with MICCAI 2022, Proceedings, volume 13611 LNCS, pp. 82 - 92
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 13611 LNCS, pp. 82 - 92
5th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, volume 13611 LNCS, pp. 82 - 92
(Part of book)
Abstract
Explainable artificial intelligence (XAI) is increasingly used to analyze the behavior of neural networks. Concept activation uses human-interpretable concepts to explain neural network behavior. This study aimed at assessing the feasibility of regression concept activation to explain detection and classification of multi-modal volumetric data. Proof-of-concept was demonstrated in metastatic prostate
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cancer patients imaged with positron emission tomography/computed tomography (PET/CT). Multi-modal volumetric concept activation was used to provide global and local explanations. Sensitivity was 80% at 1.78 false positive per patient. Global explanations showed that detection focused on CT for anatomical location and on PET for its confidence in the detection. Local explanations showed promise to aid in distinguishing true positives from false positives. Hence, this study demonstrated feasibility to explain detection and classification of multi-modal volumetric data using regression concept activation.
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Keywords: Explainable artificial intelligence, Interpretable deep learning, Medical image analysis, PET/CT, Prostate cancer, Theoretical Computer Science, General Computer Science
ISSN: 0302-9743
ISBN: 9783031179754
Publisher: Springer Science and Business Media Deutschland GmbH
Note: Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
(Peer reviewed)