AIMC Topic: Magnetic Resonance Imaging

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Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume.

Journal of Alzheimer's disease : JAD
Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey's Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV...

A Role for Prior Knowledge in Statistical Classification of the Transition from Mild Cognitive Impairment to Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
BACKGROUND: The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer's disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological ...

Evolution of Deep Learning Algorithms for MRI-Based Brain Tumor Image Segmentation.

Critical reviews in biomedical engineering
Brain tumor textures are among the most challenging features for neuroradiologists to extract from magnetic resonance images (MRIs). Exceptionally high-grade tumors such as gliomas require quick and precise diagnosis and medical intervention due to t...

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

Technology in cancer research & treatment
With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation o...

Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.

Technology in cancer research & treatment
Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning appr...

[Development of Motion Artifact Generator for Deep Learning in Brain MRI].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: We focused on deep learning for a reduction of motion artifacts in MRI. It is difficult to collect a large number of images with and without motion artifacts from clinical images. The purpose of this study was to create motion artifact image...

[Artificial intelligence in the diagnosis of prostate cancer].

Arkhiv patologii
To discuss the possibilities and prospects of using artificial intelligence (AI) in the diagnosis of prostate cancer (PC). The laboratory diagnosis of PC is considered and prostate images are analyzed according to transrectal ultrasound and magnetic ...

Sex Differences of Brain Functional Topography Revealed in Normal Aging and Alzheimer's Disease Cohort.

Journal of Alzheimer's disease : JAD
We applied graph theory analysis on resting-state functional magnetic resonance imaging data to evaluate sex differences of brain functional topography in normal controls (NCs), early mild cognitive impairment (eMCI), and AD patients. These metrics w...

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD
BACKGROUND: Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cos...

A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation.

Current medical imaging
BACKGROUND: The automatic segmentation of brain tumour from MRI medical images is mainly covered in this review. Recently, state-of-the-art performance is provided by deep learning- based approaches in the field of image classification, segmentation,...