AIMC Topic:
Magnetic Resonance Imaging
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Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists' interpretations at various levels.
Japanese journal of radiology
Apr 18, 2023
PURPOSE: To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (ResNet50) neural network constructed from different segmentations for distinguishing malignant and benign non-mass enhancement (NME) on breast magnetic r...
Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets.
NeuroImage. Clinical
Apr 17, 2023
INTRODUCTION: Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be chal...
Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation.
European radiology
Apr 15, 2023
OBJECTIVES: To verify the reliability of the volumes automatically segmented using a new artificial intelligence (AI)-based application and evaluate changes in the brain and CSF volume with healthy aging.
Brain Tumor Classification Using Deep Neural Network and Transfer Learning.
Brain topography
Apr 15, 2023
In the field of medical imaging, the classification of brain tumors based on histopathological analysis is a laborious and traditional approach. To address this issue, the use of deep learning techniques, specifically Convolutional Neural Networks (C...
Image preprocessing with contrast-limited adaptive histogram equalization improves the segmentation performance of deep learning for the articular disk of the temporomandibular joint on magnetic resonance images.
Oral surgery, oral medicine, oral pathology and oral radiology
Apr 14, 2023
OBJECTIVES: The objective was to evaluate the robustness of deep learning (DL)-based encoder-decoder convolutional neural networks (ED-CNNs) for segmenting temporomandibular joint (TMJ) articular disks using data sets acquired from 2 different 3.0-T ...
Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.
Journal of neurosurgery
Apr 14, 2023
OBJECTIVE: Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state...
A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.
Scientific data
Apr 14, 2023
Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial I...
Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients.
Current medical science
Apr 14, 2023
OBJECTIVE: This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging (MRI) for short-term postoperative facial nerve function in patients with acoustic neur...
Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.
European radiology
Apr 13, 2023
OBJECTIVES: To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation.
Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods.
World neurosurgery
Apr 13, 2023
OBJECTIVE: To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods.
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