| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Integrating uncertainty in deep neural networks for MRI based stroke analysis |
| Autor/-in: | Herzog, Lisa Murina, Elvis Dürr, Oliver Wegener, Susanne Sick, Beate |
| et. al: | No |
| DOI: | 10.1016/j.media.2020.101790 |
| Erschienen in: | Medical Image Analysis |
| Band(Heft): | 65 |
| Heft: | 101790 |
| Erscheinungsdatum: | Okt-2020 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 1361-8415 1361-8423 |
| Sprache: | Englisch |
| Schlagwörter: | Bayesian convolutional neural networks; Ischemic stroke; Magnetic resonance imaging; Uncertainty; Bayes Theorem; Humans; Magnetic Resonance Imaging; Reproducibility of Results; Neural Networks, Computer; Stroke; eess.IV; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Quantitative Biology - Quantitative Methods; Statistics - Machine Learning |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the individual image-level predictions. Those methods take advantage of the uncertainty in the image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level. |
| URI: | https://arxiv.org/ftp/arxiv/papers/2008/2008.06332.pdf https://digitalcollection.zhaw.ch/handle/11475/30395 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Herzog, L., Murina, E., Dürr, O., Wegener, S., & Sick, B. (2020). Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical Image Analysis, 65(101790). https://doi.org/10.1016/j.media.2020.101790
Herzog, L. et al. (2020) ‘Integrating uncertainty in deep neural networks for MRI based stroke analysis’, Medical Image Analysis, 65(101790). Available at: https://doi.org/10.1016/j.media.2020.101790.
L. Herzog, E. Murina, O. Dürr, S. Wegener, and B. Sick, “Integrating uncertainty in deep neural networks for MRI based stroke analysis,” Medical Image Analysis, vol. 65, no. 101790, Oct. 2020, doi: 10.1016/j.media.2020.101790.
HERZOG, Lisa, Elvis MURINA, Oliver DÜRR, Susanne WEGENER und Beate SICK, 2020. Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical Image Analysis [online]. Oktober 2020. Bd. 65, Nr. 101790. DOI 10.1016/j.media.2020.101790. Verfügbar unter: https://arxiv.org/ftp/arxiv/papers/2008/2008.06332.pdf
Herzog, Lisa, Elvis Murina, Oliver Dürr, Susanne Wegener, and Beate Sick. 2020. “Integrating Uncertainty in Deep Neural Networks for MRI Based Stroke Analysis.” Medical Image Analysis 65 (101790). https://doi.org/10.1016/j.media.2020.101790.
Herzog, Lisa, et al. “Integrating Uncertainty in Deep Neural Networks for MRI Based Stroke Analysis.” Medical Image Analysis, vol. 65, no. 101790, Oct. 2020, https://doi.org/10.1016/j.media.2020.101790.
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