Publications by authors named "Julia Moosbauer"
Article Synopsis
- The success of deep learning in computational biology relies on specific architecture design, but there's no agreed-upon optimal approach, often borrowing from computer vision, which may ignore genomic features.
- GenomeNet-Architect is introduced as a framework that automatically optimizes neural network architectures specifically for genome sequence data, enhancing the design and hyperparameter tuning.
- In a viral classification task, GenomeNet-Architect improved accuracy by reducing misclassification rates by 19%, providing 67% faster processing, and achieving similar model performance with 83% fewer parameters compared to existing leading methods.
Introduction: Double inversion recovery (DIR) has been validated as a sensitive magnetic resonance imaging (MRI) contrast in multiple sclerosis (MS). Deep learning techniques can use basic input data to generate synthetic DIR (synthDIR) images that are on par with their acquired counterparts. As assessment of longitudinal MRI data is paramount in MS diagnostics, our study's purpose is to evaluate the utility of synthDIR longitudinal subtraction imaging for detection of disease progression in a multicenter data set of MS patients.
View Article and Find Full Text PDFBackground: Most artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a learned norm. Here, we investigate whether diagnostic accuracy and reporting times can be improved by an anomaly detection tool for head computed tomography (CT), tailored to provide patient-level triage and voxel-based highlighting of pathologies.
View Article and Find Full Text PDFPurpose: Advanced machine-learning (ML) techniques can potentially detect the entire spectrum of pathology through deviations from a learned norm. We investigated the utility of a weakly supervised ML tool to detect characteristic findings related to ischemic stroke in head CT and provide subsequent patient triage.
Methods: Patients having undergone non-enhanced head CT at a tertiary care hospital in April 2020 with either no anomalies, subacute or chronic ischemia, lacunar infarcts of the deep white matter or hyperdense vessel signs were retrospectively analyzed.
Objectives: Anomaly detection systems can potentially uncover the entire spectrum of pathologies through deviations from a learned norm, meaningfully supporting the radiologist's workflow. We aim to report on the utility of a weakly supervised machine learning (ML) tool to detect pathologies in head computed tomography (CT) and adequately triage patients in an unselected patient cohort.
Materials And Methods: All patients having undergone a head CT at a tertiary care hospital in March 2020 were eligible for retrospective analysis.