AIMC Topic: Magnetic Resonance Imaging
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Predicting Hypoperfusion Lesion and Target Mismatch in Stroke from Diffusion-weighted MRI Using Deep Learning.
Radiology
Dec 6, 2022
Background Perfusion imaging is important to identify a target mismatch in stroke but requires contrast agents and postprocessing software. Purpose To use a deep learning model to predict the hypoperfusion lesion in stroke and identify patients with ...
Development of a Prediction Model for Positive Surgical Margin in Robot-Assisted Laparoscopic Radical Prostatectomy.
Current oncology (Toronto, Ont.)
Dec 6, 2022
A positive surgical margin (PSM) is reported to have some connection to the occurrence of biochemical recurrence and tumor metastasis in prostate cancer after the operation. There are no clinically usable models and the study is to predict the probab...
PI-RADS v2 Findings of MRI and Positive Biopsy Core Percentage would Predict Pathological Extraprostatic Extension in Patients who Underwent Robot Assisted Radical Prostatectomy: A Retrospective Study.
Urology journal
Dec 6, 2022
PURPOSE: This study aimed to examine whether preoperative Prostate Imaging Reporting and Data System v2 (PI-RADS v2) can predict pathological extracapsular extension (EPE) after radical prostatectomy. We also studied the preoperative factors which ca...
An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.
Magnetic resonance in medicine
Dec 5, 2022
PURPOSE: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.
Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics.
Journal of neurosurgery
Dec 2, 2022
OBJECTIVE: The aim of this study was to build a convolutional neural network (CNN)-based prediction model of glioblastoma (GBM) molecular subtype diagnosis and prognosis with multimodal features.
Prior Attention Network for Multi-Lesion Segmentation in Medical Images.
IEEE transactions on medical imaging
Dec 2, 2022
The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However...
Deep problems with neural network models of human vision.
The Behavioral and brain sciences
Dec 1, 2022
Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more ...
Efficient framework for brain tumor detection using different deep learning techniques.
Frontiers in public health
Dec 1, 2022
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals...
VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer's disease prediction.
Computer methods and programs in biomedicine
Nov 30, 2022
BACKGROUND AND OBJECTIVE: Mild cognitive impairment (MCI) is a transitional state between normal aging and Alzheimer's disease (AD), and accurately predicting the progression trend of MCI is critical to the early prevention and treatment of AD. Brain...
Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank.
Journal of the American Heart Association
Nov 29, 2022
Background Automated analysis of cardiovascular magnetic resonance images provides the potential to assess aortic distensibility in large populations. The aim of this study was to compare the prediction of cardiovascular events by automated cardiovas...
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