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2022-11-14Kumulative Dissertation DOI: 10.18452/25413
Explainable deep learning classifiers for disease detection based on structural brain MRI data
dc.contributor.authorEitel, Fabian
dc.date.accessioned2022-11-14T12:18:57Z
dc.date.available2022-11-14T12:18:57Z
dc.date.issued2022-11-14none
dc.identifier.urihttp://edoc.hu-berlin.de/18452/26155
dc.description.abstractIn dieser Doktorarbeit wird die Frage untersucht, wie erfolgreich deep learning bei der Diagnostik von neurodegenerativen Erkrankungen unterstützen kann. In 5 experimentellen Studien wird die Anwendung von Convolutional Neural Networks (CNNs) auf Daten der Magnetresonanztomographie (MRT) untersucht. Ein Schwerpunkt wird dabei auf die Erklärbarkeit der eigentlich intransparenten Modelle gelegt. Mit Hilfe von Methoden der erklärbaren künstlichen Intelligenz (KI) werden Heatmaps erstellt, die die Relevanz einzelner Bildbereiche für das Modell darstellen. Die 5 Studien dieser Dissertation zeigen das Potenzial von CNNs zur Krankheitserkennung auf neurologischen MRT, insbesondere bei der Kombination mit Methoden der erklärbaren KI. Mehrere Herausforderungen wurden in den Studien aufgezeigt und Lösungsansätze in den Experimenten evaluiert. Über alle Studien hinweg haben CNNs gute Klassifikationsgenauigkeiten erzielt und konnten durch den Vergleich von Heatmaps zur klinischen Literatur validiert werden. Weiterhin wurde eine neue CNN Architektur entwickelt, spezialisiert auf die räumlichen Eigenschaften von Gehirn MRT Bildern.ger
dc.description.abstractDeep learning and especially convolutional neural networks (CNNs) have a high potential of being implemented into clinical decision support software for tasks such as diagnosis and prediction of disease courses. This thesis has studied the application of CNNs on structural MRI data for diagnosing neurological diseases. Specifically, multiple sclerosis and Alzheimer’s disease were used as classification targets due to their high prevalence, data availability and apparent biomarkers in structural MRI data. The classification task is challenging since pathology can be highly individual and difficult for human experts to detect and due to small sample sizes, which are caused by the high acquisition cost and sensitivity of medical imaging data. A roadblock in adopting CNNs to clinical practice is their lack of interpretability. Therefore, after optimizing the machine learning models for predictive performance (e.g. balanced accuracy), we have employed explainability methods to study the reliability and validity of the trained models. The deep learning models achieved good predictive performance of over 87% balanced accuracy on all tasks and the explainability heatmaps showed coherence with known clinical biomarkers for both disorders. Explainability methods were compared quantitatively using brain atlases and shortcomings regarding their robustness were revealed. Further investigations showed clear benefits of transfer-learning and image registration on the model performance. Lastly, a new CNN layer type was introduced, which incorporates a prior on the spatial homogeneity of neuro-MRI data. CNNs excel when used on natural images which possess spatial heterogeneity, and even though MRI data and natural images share computational similarities, the composition and orientation of neuro-MRI is very distinct. The introduced patch-individual filter (PIF) layer breaks the assumption of spatial invariance of CNNs and reduces convergence time on different data sets without reducing predictive performance. The presented work highlights many challenges that CNNs for disease diagnosis face on MRI data and defines as well as tests strategies to overcome those.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.relation.haspart10.3389/fnagi.2019.00194
dc.relation.haspart10.1016/j.nicl.2019.102003
dc.relation.haspart10.1007/978-3-030-33850-3_1
dc.relation.haspart10.1007/978-3-030-87586-2_5
dc.relation.haspart10.1038/s41598-021-03785-9
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectmaschinelles lernenger
dc.subjectCNNger
dc.subjectNeurobildgebungger
dc.subjectKünstliche Intelligenzger
dc.subjectKrankheitsdiagnoseger
dc.subjectMRTger
dc.subjectCNNeng
dc.subjectmachine learningeng
dc.subjectdeep learningeng
dc.subjectneuroimagingeng
dc.subjectMRIeng
dc.subjectdisease detectioneng
dc.subject.ddc150 Psychologienone
dc.subject.ddc000 Informatik, Informationswissenschaft, allgemeine Werkenone
dc.titleExplainable deep learning classifiers for disease detection based on structural brain MRI datanone
dc.typedoctoralThesis
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/26155-7
dc.identifier.doihttp://dx.doi.org/10.18452/25413
dc.date.accepted2022-10-05
dc.contributor.refereeRitter, Kerstin
dc.contributor.refereeStober, Sebastian
dc.contributor.refereeMarkett, Sebastian
dc.subject.rvkYG 4004
dc.subject.rvkCZ 1360
dc.subject.rvkYG 6004
local.edoc.pages108none
local.edoc.type-nameKumulative Dissertation
bua.departmentLebenswissenschaftliche Fakultätnone

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