Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning

Xiajun Jiang, Zhiyuan Li, Ryan Missel, Md Shakil Zaman, Brian Zenger, Wilson W. Good, Rob S. MacLeod, John L. Sapp, Linwei Wang

Résultat de recherche: Conference contribution

2 Citations (Scopus)

Résumé

Clinical adoption of personalized virtual heart simulations faces challenges in model personalization and expensive computation. While an ideal solution is an efficient neural surrogate that at the same time is personalized to an individual subject, the state-of-the-art is either concerned with personalizing an expensive simulation model, or learning an efficient yet generic surrogate. This paper presents a completely new concept to achieve personalized neural surrogates in a single coherent framework of meta-learning (metaPNS). Instead of learning a single neural surrogate, we pursue the process of learning a personalized neural surrogate using a small amount of context data from a subject, in a novel formulation of few-shot generative modeling underpinned by: 1) a set-conditioned neural surrogate for cardiac simulation that, conditioned on subject-specific context data, learns to generate query simulations not included in the context set, and 2) a meta-model of amortized variational inference that learns to condition the neural surrogate via simple feed-forward embedding of context data. As test time, metaPNS delivers a personalized neural surrogate by fast feed-forward embedding of a small and flexible number of data available from an individual, achieving – for the first time – personalization and surrogate construction for expensive simulations in one end-to-end learning framework. Synthetic and real-data experiments demonstrated that metaPNS was able to improve personalization and predictive accuracy in comparison to conventionally-optimized cardiac simulation models, at a fraction of computation.

Langue d'origineEnglish
Titre de la publication principaleMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
ÉditeursLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Maison d'éditionSpringer Science and Business Media Deutschland GmbH
Pages46-56
Nombre de pages11
ISBN (imprimé)9783031164514
DOI
Statut de publicationPublished - 2022
Événement25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Durée: sept. 18 2022sept. 22 2022

Séries de publication

PrénomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13438 LNCS
ISSN (imprimé)0302-9743
ISSN (électronique)1611-3349

Conference

Conference25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Pays/TerritoireSingapore
VilleSingapore
Période9/18/229/22/22

Note bibliographique

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

Empreinte numérique

Plonger dans les sujets de recherche 'Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning'. Ensemble, ils forment une empreinte numérique unique.

Citer