Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
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Explaining deep neural networks for knowledge discovery in electrocardiogram analysis. / Hicks, Steven A; Isaksen, Jonas L; Thambawita, Vajira; Ghouse, Jonas; Ahlberg, Gustav; Linneberg, Allan; Grarup, Niels; Strümke, Inga; Ellervik, Christina; Olesen, Morten Salling; Hansen, Torben; Graff, Claus; Holstein-Rathlou, Niels-Henrik; Halvorsen, Pål; Maleckar, Mary M; Riegler, Michael A; Kanters, Jørgen K.
I: Scientific Reports, Bind 11, Nr. 1, 10949, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
AU - Hicks, Steven A
AU - Isaksen, Jonas L
AU - Thambawita, Vajira
AU - Ghouse, Jonas
AU - Ahlberg, Gustav
AU - Linneberg, Allan
AU - Grarup, Niels
AU - Strümke, Inga
AU - Ellervik, Christina
AU - Olesen, Morten Salling
AU - Hansen, Torben
AU - Graff, Claus
AU - Holstein-Rathlou, Niels-Henrik
AU - Halvorsen, Pål
AU - Maleckar, Mary M
AU - Riegler, Michael A
AU - Kanters, Jørgen K
PY - 2021
Y1 - 2021
N2 - Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.
AB - Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.
U2 - 10.1038/s41598-021-90285-5
DO - 10.1038/s41598-021-90285-5
M3 - Journal article
C2 - 34040033
VL - 11
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 10949
ER -
ID: 269904130