RPS 405 - Towards a comprehensible convolutional neural network for the detection of Alzheimer’s disease in magnetic resonance images
05:02D. Cantré, Rostock / Germany
2
RPS 405 - Detection of ischaemic stroke presence and classification of territorial type in diffusion-weighted MRI by deep learning
05:56I. Koska, Izmir / Turkey
3
RPS 405 - Multivariable diagnostic prediction model to detect hydrocephaly from T2-weighted MRI radiomics and conventional features with artificial neural networks in colloid cysts
04:12M. Doğan, Istanbul / Turkey
4
RPS 405 - The predictive power of MRI VASARI features for identifying the mutation status of multiple genes in glioblastoma using machine learning algorithms
05:00A. Haddadi Avval, Mashhad / Iran
5
RPS 405 - Application of deep learning for a reliable MRI-based diagnosis for progressive supranuclear palsy (PSP)
04:29J. Opalka, Berlin / Germany
6
RPS 405 - Aneurysmal subarachnoid haemorrhage: deep learning assessment vs radiologist in aneurysm size and volume
04:16L. Pennig, Cologne / Germany
7
RPS 405 - Clinical performance of a deep learning model for detection of aneurysms in subarachnoid haemorrhage compared to human readers
04:27L. Pennig, Cologne / Germany
8
RPS 405 - K-space based deep learning reconstruction empowers 50% acceleration of MR spine imaging: a prospective multicentre multireader trial
08:51L. Tanenbaum, New York, NY / United States
9
RPS 405 - Deep learning enables 60% accelerated volumetric brain MRI while preserving quantitative performance: a prospective multicentre multireader trial
05:24S. Bash, Woodland Hills, CA / United States
10
RPS 405 - Automated segmentation and reconstruction of the optic nerves and chiasm in MR images using deep learning
03:54Y. Zhang, Chengdu / China
11
RPS 405 - Multiparametric deep learning model segmentation for glioblastoma on multi-institutional clinical trial data sets: feasibility, opportunities and challenges
06:23M. Knopp, Columbus, OH / United States
12
RC 405 - Volumetric analysis of tumour compartments on longitudinal MRI of glioblastoma patients using deep learning
06:13M. Perkuhn, Cologne / Germany
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