SM2N2: A Stacked Architecture for Multimodal Data and Its Application to Myocardial Infarction Detection

Rishabh Sharma, Christoph F. Eick, Nikolaos V. Tsekos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This work introduces a novel Stacked Multimodal (SM2N2) architecture and assess its performance in classifying whether a patient have or not Myocardial Infarction. Central to this SM2N2 architecture is the use of images and clinical data as input. Comparison studies of Multimodal Neural Network (M2N2) component of SM2N2 with AlexNet3D model demonstrated that on small size dataset, M2N2 is faster, has less trainable parameters and results higher accuracy in this binary classification. In addition to M2N2 we also identify clinical features that are sufficient to classify normal vs pathological cases. We also train statistical models on identified clinical features and use stacking to combine outputs from statistical models and M2N2. Stacking generalizes the results and the new model learns how to best combine the results of the individual base models. One of the potential application of the M2N2 is that because of less parameters the network can be deployed on mobile devices for inference.

Original languageEnglish (US)
Title of host publicationStatistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
EditorsEsther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
PublisherSpringer Science and Business Media Deutschland GmbH
Pages342-350
Number of pages9
ISBN (Print)9783030681067
DOIs
StatePublished - 2021
Event11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12592 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020
Country/TerritoryPeru
CityLima
Period10/4/2010/4/20

Keywords

  • Classification
  • Delayed-enhancement
  • Heart
  • MRI
  • Myocardial infarction
  • Normal case

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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