Titlebook: Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries; 8th International Wo Spyridon Bakas,Alessandro Crimi,Reuben Dor

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Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries978-3-031-33842-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

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978-3-031-33841-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Abdul Ghafar Ismail,Zuriyati Ahmadlity estimation models. We evaluate on a complex multi-class segmentation problem, specifically glioma segmentation, following the BraTS annotation protocol. The training data features quality ratings from 15 expert neuroradiologists on a scale ranging from 1 to 6 stars for various computer-generate

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https://doi.org/10.1007/978-3-319-30445-8aining. Most commonly, the anomaly detection model generates a “normal” version of an input image, and the pixel-wise .-difference of the two is used to localize anomalies. However, large residuals often occur due to imperfect reconstruction of the complex anatomical structures present in most medic

Graves’-disease

Rodolfo Wehrhahn,Nadège Jassaudecursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim to increase diagnostic efficiency by replacing a single application with generalized algorithms. The goal of unsupervised anomaly detection (UAD) is to identify potential anomalous regions unseen during trai

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Macroprudential Supervision in Insuranceodel weights. Two central problems arise when sending the updated weights to the central node in a federation: the imbalance of the datasets and data heterogeneity caused by differences in scanners or acquisition protocols. In this paper, we benchmark the federated average algorithm and adapt two we

refraction

Sebastian von Dahlen,Marcelo Ramella-fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Longitudinal brain FLAIR MRI in MS, involving repetitively imaging a patient over time, provides helpful informati

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Macroprudential Supervision in Insuranceraining data. Contrarily, medical imaging deals with 3D data and usually lacks the equivalent extent and diversity of data, for developing AI models. Transfer learning provides the means to use models trained for one application as a starting point to another application. In this work, we leverage 2