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Explainable Deep Learning for Myocardial Perfusion Imaging with a Grad-CAM-Based Loss Function

Karlsson, Jennie LU ; Heyden, Anders LU orcid ; Åström, Karl LU orcid ; Overgaard, Niels Christian LU ; Dahl, Julia ; Davidsson, Anette ; Figueroa, Miguel Ochoa and Arvidsson, Ida LU orcid (2026) Medical Imaging 2026: Computer-Aided Diagnosis In Progress in Biomedical Optics and Imaging - Proceedings of SPIE 13926.
Abstract

As deep learning methods become more frequent in medical imaging, model explainability becomes essential in order to ensure trust. Gradient-weighted class activation mapping (Grad-CAM) is a widely used technique for visualizing which regions of an input contribute to a network’s decision. In this work, we introduce a novel Grad-CAM–based loss function that leverages prior knowledge of target regions to constrain and improve the relevance assigned by Grad-CAM. The approach is applied to myocardial perfusion imaging (MPI), a standard examination method for evaluating suspected coronary artery disease (CAD). When CAD is suspected, invasive coronary angiography is usually performed to assess the degree of stenosis using quantitative... (More)

As deep learning methods become more frequent in medical imaging, model explainability becomes essential in order to ensure trust. Gradient-weighted class activation mapping (Grad-CAM) is a widely used technique for visualizing which regions of an input contribute to a network’s decision. In this work, we introduce a novel Grad-CAM–based loss function that leverages prior knowledge of target regions to constrain and improve the relevance assigned by Grad-CAM. The approach is applied to myocardial perfusion imaging (MPI), a standard examination method for evaluating suspected coronary artery disease (CAD). When CAD is suspected, invasive coronary angiography is usually performed to assess the degree of stenosis using quantitative coronary angiography (QCA). A convolutional neural network (CNN) was trained to predict QCA values for the three main coronary arteries of the heart (LAD, RCA, LCx) from MPI. When training the CNN with the proposed loss function, area under the receiver operating characteristics curve (AUC) values of 79.2%, 93.1%, 86.2% were achieved for LAD, RCA, and LCx respectively. The proposed loss function remains competitive to standardized functions, while also producing heatmaps which less frequently assign relevance to incorrect regions.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Convolutional neural networks, Explainability, Grad-CAM, Myocardial perfusion imaging
host publication
Medical Imaging 2026 : Computer-Aided Diagnosis - Computer-Aided Diagnosis
series title
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
editor
Wismuller, Axel and Deserno, Thomas Martin
volume
13926
article number
1392629
publisher
SPIE
conference name
Medical Imaging 2026: Computer-Aided Diagnosis
conference location
Vancouver, Canada
conference dates
2026-02-15 - 2026-02-19
external identifiers
  • scopus:105041009737
ISSN
1605-7422
2410-9045
ISBN
9781510697898
DOI
10.1117/12.3086120
language
English
LU publication?
yes
id
4d7b1036-f52a-4cf9-9a78-efbb0ff6613d
date added to LUP
2026-08-24 14:19:53
date last changed
2026-08-25 03:05:46
@inproceedings{4d7b1036-f52a-4cf9-9a78-efbb0ff6613d,
  abstract     = {{<p>As deep learning methods become more frequent in medical imaging, model explainability becomes essential in order to ensure trust. Gradient-weighted class activation mapping (Grad-CAM) is a widely used technique for visualizing which regions of an input contribute to a network’s decision. In this work, we introduce a novel Grad-CAM–based loss function that leverages prior knowledge of target regions to constrain and improve the relevance assigned by Grad-CAM. The approach is applied to myocardial perfusion imaging (MPI), a standard examination method for evaluating suspected coronary artery disease (CAD). When CAD is suspected, invasive coronary angiography is usually performed to assess the degree of stenosis using quantitative coronary angiography (QCA). A convolutional neural network (CNN) was trained to predict QCA values for the three main coronary arteries of the heart (LAD, RCA, LCx) from MPI. When training the CNN with the proposed loss function, area under the receiver operating characteristics curve (AUC) values of 79.2%, 93.1%, 86.2% were achieved for LAD, RCA, and LCx respectively. The proposed loss function remains competitive to standardized functions, while also producing heatmaps which less frequently assign relevance to incorrect regions.</p>}},
  author       = {{Karlsson, Jennie and Heyden, Anders and Åström, Karl and Overgaard, Niels Christian and Dahl, Julia and Davidsson, Anette and Figueroa, Miguel Ochoa and Arvidsson, Ida}},
  booktitle    = {{Medical Imaging 2026 : Computer-Aided Diagnosis}},
  editor       = {{Wismuller, Axel and Deserno, Thomas Martin}},
  isbn         = {{9781510697898}},
  issn         = {{1605-7422}},
  keywords     = {{Convolutional neural networks; Explainability; Grad-CAM; Myocardial perfusion imaging}},
  language     = {{eng}},
  month        = {{04}},
  publisher    = {{SPIE}},
  series       = {{Progress in Biomedical Optics and Imaging - Proceedings of SPIE}},
  title        = {{Explainable Deep Learning for Myocardial Perfusion Imaging with a Grad-CAM-Based Loss Function}},
  url          = {{http://dx.doi.org/10.1117/12.3086120}},
  doi          = {{10.1117/12.3086120}},
  volume       = {{13926}},
  year         = {{2026}},
}