AIMC Topic: Magnetic Resonance Imaging
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MRI denoising with a non-blind deep complex-valued convolutional neural network.
NMR in biomedicine
Nov 11, 2024
MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal...
Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.
BMC medical imaging
Nov 11, 2024
BACKGROUND: This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI).
Grade prediction of lesions in cerebral white matter using a convolutional neural network.
PloS one
Nov 11, 2024
We established a diagnostic method for cerebral white matter lesions using MRI images and examined the relationship between the MRI images and the medical checkup data. There were approximately 25 MRI images for each patient's head, from the top of t...
Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.
PloS one
Nov 11, 2024
BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality an...
Unveiling the decision making process in Alzheimer's disease diagnosis: A case-based counterfactual methodology for explainable deep learning.
Journal of neuroscience methods
Nov 9, 2024
BACKGROUND: The field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) models. While DL surpasses traditional machine learning in disease prediction from structural magnetic r...
Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.
Medical image analysis
Nov 8, 2024
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, la...
Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: Machine learning applied to resting-state fMRI.
Cortex; a journal devoted to the study of the nervous system and behavior
Nov 8, 2024
Diagnosis of dyslexia often occurs in late schooling years, leading to academic and psychological challenges. Furthermore, diagnosis is time-consuming, costly, and reliant on arbitrary cutoffs. On the other hand, automated algorithms hold great poten...
Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time.
Neuroradiology
Nov 8, 2024
INTRODUCTION: Assessment of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. We evaluate whether assessment of new, expanding, and contrast-enhancing MS lesions can be done more time-eff...
Segmentation and classification of brain tumor using Taylor fire hawk optimization enabled deep learning approach.
Electromagnetic biology and medicine
Nov 8, 2024
The brain is a crucial organ that controls the body's neural system. The tumor develops and spreads across the brain as a result of irregular cell generation. The provision of substantial treatment to patients requires the early diagnosis of malignan...
Comprehensive Morphometric Analysis to Identify Key Neuroimaging Biomarkers for the Diagnosis of Adult Hydrocephalus Using Artificial Intelligence.
Neurosurgery
Nov 7, 2024
BACKGROUND AND OBJECTIVES: Hydrocephalus involves abnormal cerebrospinal fluid accumulation in brain ventricles. Early and accurate diagnosis is crucial for timely intervention and preventing progressive neurological deterioration. The aim of this st...
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