• Media type: E-Book; Thesis
  • Title: Toward Trustworthiness of Deep Learning Models for 12-Lead ECGs
  • Contributor: Bender, Theresa [VerfasserIn]; Sax, Ulrich [AkademischeR BetreuerIn]; Parlitz, Ulrich [AkademischeR BetreuerIn]; Tolxdorff, Thomas [AkademischeR BetreuerIn]
  • imprint: Göttingen, 2024
  • Extent: 1 Online-Ressource; Illustrationen, Diagramme
  • Language: English
  • Identifier:
  • Keywords: Biosignal Processing ; Deep Learning ; Electrocardiogram ; Trustworthiness ; Explainability ; Robustness ; Hochschulschrift
  • Origination:
  • University thesis: Dissertation, Georg-August-Universität Göttingen, 2023
  • Footnote:
  • Description: A 12-lead electrocardiogram (ECG), a common examination tool in cardiology, represents the electrical activity of the heart as waveforms. Predictions and classifications with deep learning (DL) algorithms show great potential to aid clinicians in the diagnosis and treatment of patients. However, since clinicians are responsible for the treatment and thus the outcome of single patients, they need to understand the reasoning behind these model’s decisions. Important criteria for the acceptance of DL models in clinical settings are covered by aspects of trustworthiness, such as safety and priv...
  • Access State: Open Access
  • Rights information: Attribution (CC BY)