@article {43321,
	title = {Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis},
	journal = {Diagnostics},
	volume = {13},
	number = {2345},
	year = {2023},
	month = {07/2023},
	publisher = {MDPI},
	abstract = {Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of {\textquotedblleft}black box{\textquotedblright} models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes.},
	keywords = {electrocardiograms, Explainable artificial intelligence, heat maps},
	doi = {https://doi.org/10.3390/diagnostics13142345},
	author = {Stor{\r a}s, Andrea and Ole Emil {Andersen} and Lockhart, Sam and Thielemann, Roman and Gnesin, Filip and Thambawita, Vajira and Hicks, Steven and Kanters, J{\o}rgen and Str{\"u}mke, Inga and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {27834,
	title = {DeepSynthBody: the beginning of the end for data deficiency in medicine},
	journal = {The International Conference on Applied Artificial Intelligence (ICAPAI)},
	year = {2021},
	publisher = {IEEE},
	abstract = {Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.},
	doi = {10.1109/ICAPAI49758.2021.9462062},
	author = {Thambawita, Vajira and Hicks, Steven and Isaksen, Jonas and Mette Haug {Stensen} and Trine B {Haugen} and Kanters, J{\o}rgen and Parasa, Sravanthi and de Lange, Thomas and H{\r a}vard D. {Johansen} and Johansen, Dag and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
