AIMC Topic: Brain
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Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.
Journal of imaging informatics in medicine
Feb 12, 2024
The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patient...
Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping.
Magnetic resonance in medicine
Feb 11, 2024
PURPOSE: This paper proposes a novel self-supervised learning framework that uses model reinforcement, REference-free LAtent map eXtraction with MOdel REinforcement (RELAX-MORE), for accelerated quantitative MRI (qMRI) reconstruction. The proposed me...
A deep learning approach for mental health quality prediction using functional network connectivity and assessment data.
Brain imaging and behavior
Feb 10, 2024
While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we...
Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods.
Behavior genetics
Feb 10, 2024
Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to e...
What have we learned about artificial intelligence from studying the brain?
Biological cybernetics
Feb 10, 2024
Neuroscience and artificial intelligence (AI) share a long, intertwined history. It has been argued that discoveries in neuroscience were (and continue to be) instrumental in driving the development of new AI technology. Scrutinizing these historical...
Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.
Molecular psychiatry
Feb 9, 2024
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psy...
NeoSLAM: Long-Term SLAM Using Computational Models of the Brain.
Sensors (Basel, Switzerland)
Feb 9, 2024
Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaini...
Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances.
Talanta
Feb 7, 2024
Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depe...
White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.
Molecular psychiatry
Feb 7, 2024
White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the gener...
Safety and efficacy of frameless stereotactic robot-assisted intraparenchymal brain lesion biopsies versus image-guided biopsies: a bicentric comparative study.
Acta neurochirurgica
Feb 6, 2024
PURPOSE: User-friendly robotic assistance and image-guided tools have been developed in the past decades for intraparenchymal brain lesion biopsy. These two methods are gradually becoming well accepted and are performed at the discretion of the neuro...
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