Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model

Chiara Mauri; Stefano Cerri; Oula Puonti; Mark Mühlau; Koen Van Leemput

doi:10.1007/978-3-031-16452-1_43

Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model

Chiara Mauri^*, Stefano Cerri, Oula Puonti, Mark Mühlau, Koen Van Leemput

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject’s age, from individual brain scans. Although the field has focused strongly on nonlinear discriminative methods using deep learning, here we explore whether linear generative techniques can be used as practical alternatives that are easier to tune, train and interpret. The models we propose consist of (1) a causal forward model expressing the effect of variables of interest on brain morphology, and (2) a latent variable noise model, based on factor analysis, that is quick to learn and invert. In experiments estimating individuals’ age and gender from the UK Biobank dataset, we demonstrate competitive prediction performance even when the number of training subjects is in the thousands – the typical scenario in many potential applications. The method is easy to use as it has only a single hyperparameter, and directly estimates interpretable spatial maps of the underlying structural changes that are driving the predictions.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Editors	Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Publisher	Springer Nature Switzerland AG
Publication date	2022
Pages	448-458
ISBN (Print)	9783031164514
DOIs	https://doi.org/10.1007/978-3-031-16452-1_43
Publication status	Published - 2022
Event	25th International Conference on Medical Image Computing and Computer-Assisted Intervention - Resorts World Convention Centre Singapore, Singapore, Singapore Duration: 18 Sept 2022 → 22 Sept 2022 Conference number: 25 https://conferences.miccai.org/2022/en/

Conference

Conference	25th International Conference on Medical Image Computing and Computer-Assisted Intervention
Number	25
Location	Resorts World Convention Centre Singapore
Country/Territory	Singapore
City	Singapore
Period	18/09/2022 → 22/09/2022
Internet address	https://conferences.miccai.org/2022/en/

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13438 LNCS
ISSN	0302-9743

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Mauri, C., Cerri, S., Puonti, O., Mühlau, M., & Van Leemput, K. (2022). Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (pp. 448-458). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-16452-1_43

Mauri, Chiara ; Cerri, Stefano ; Puonti, Oula et al. / Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 . editor / Linwei Wang ; Qi Dou ; P. Thomas Fletcher ; Stefanie Speidel ; Shuo Li. Springer Nature Switzerland AG, 2022. pp. 448-458 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13438 LNCS).

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Mauri, C, Cerri, S, Puonti, O, Mühlau, M & Van Leemput, K 2022, Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model. in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 . Springer Nature Switzerland AG, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13438 LNCS, pp. 448-458, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, Singapore, Singapore, 18/09/2022. https://doi.org/10.1007/978-3-031-16452-1_43

Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model. / Mauri, Chiara; Cerri, Stefano; Puonti, Oula et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 . ed. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. Springer Nature Switzerland AG, 2022. p. 448-458 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13438 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AB - Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject’s age, from individual brain scans. Although the field has focused strongly on nonlinear discriminative methods using deep learning, here we explore whether linear generative techniques can be used as practical alternatives that are easier to tune, train and interpret. The models we propose consist of (1) a causal forward model expressing the effect of variables of interest on brain morphology, and (2) a latent variable noise model, based on factor analysis, that is quick to learn and invert. In experiments estimating individuals’ age and gender from the UK Biobank dataset, we demonstrate competitive prediction performance even when the number of training subjects is in the thousands – the typical scenario in many potential applications. The method is easy to use as it has only a single hyperparameter, and directly estimates interpretable spatial maps of the underlying structural changes that are driving the predictions.

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Mauri C, Cerri S, Puonti O, Mühlau M, Van Leemput K. Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model. In Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 . Springer Nature Switzerland AG. 2022. p. 448-458. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13438 LNCS). doi: 10.1007/978-3-031-16452-1_43