Machine Learning in Magnetic Resonance Images of Glioblastoma: A Review.

Background: The purpose of this work was to identify which Glioblastoma (GBM) problems can be handled by Magnetic Resonance Imaging (MRI) and Machine Learning (ML) techniques. Results, limitations, and trends through a review of the scientific literature in the last 5 years were performed. Google Scholar, PubMed, Elsevier databases, and forward and backward citations were used for searching articles applying ML techniques in GBM.

Machine learning for condensed matter physics.

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many other important branches of science, such as chemistry, materials science, statistical physics, and high-performance computing. With the advancements in modern machine learning (ML) technology, a keen interest in applying these algorithms to further CMP research has created a compelling new area of research at the intersection of both fields.

Auditory impairment in H-ABC tubulinopathy.

Hypomyelination with atrophy of the basal ganglia and cerebellum (H-ABC) is a neurodegenerative disease due to mutations in TUBB4A. Patients suffer from extrapyramidal movements, spasticity, ataxia, and cognitive deficits. Magnetic resonance imaging features are hypomyelination and atrophy of the striatum and cerebellum.