AIMC Topic:
Image Interpretation, Computer-Assisted

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PCNN Mechanism and its Parameter Settings.

IEEE transactions on neural networks and learning systems Apr 11, 2019
The pulse-coupled neural network (PCNN) model is a third-generation artificial neural network without training that uses the synchronous pulse bursts of neurons to process digital images, but the lack of in-depth theoretical research limits its exten...
Models, Theoretical Models, Neurological Neurons Image Interpretation, Computer-Assisted Neural Networks, Computer Electrophysiological Phenomena Algorithms Image Processing, Computer-Assisted
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A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.

Scientific reports Apr 11, 2019
This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the f...
Aged Young Adult Adult Male Middle Aged Kidney Transplantation Graft Rejection Postoperative Complications Prognosis Graft Survival Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Humans Female Diffusion Magnetic Resonance Imaging Adolescent Glomerular Filtration Rate Risk Factors Neural Networks, Computer Kidney Function Tests Algorithms Follow-Up Studies
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Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.

Magnetic resonance in medicine Apr 8, 2019
PURPOSE: Radiomics allows for powerful data-mining and feature extraction techniques to guide clinical decision making. Image segmentation is a necessary step in such pipelines and different techniques can significantly affect results. We demonstrate...
Radiologists Humans Neural Networks, Computer Breast Female Databases, Factual Image Interpretation, Computer-Assisted Breast Neoplasms Magnetic Resonance Imaging
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A knowledge-based system for brain tumor segmentation using only 3D FLAIR images.

Australasian physical & engineering sciences in medicine Apr 8, 2019
This study aims to develop a semi-automatic system for brain tumor segmentation in 3D MR images. For a given image, noise was corrected using SUSAN algorithm first. A specific region of interest (ROI) that contains tumor was identified and then the i...
Algorithms Brain Neoplasms Imaging, Three-Dimensional Knowledge Bases Image Interpretation, Computer-Assisted Humans
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Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network.

Magnetic resonance imaging Apr 4, 2019
For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE fo...
Neural Networks, Computer Brain Humans Female Male Adult Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Neuroimaging
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Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.

Neuroradiology Apr 4, 2019
PURPOSE: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this...
Magnetic Resonance Imaging Diagnosis, Differential Glioblastoma Image Interpretation, Computer-Assisted Male Middle Aged Adolescent Lymphoma Aged, 80 and over Machine Learning Adult Meningioma Humans Prospective Studies Sensitivity and Specificity Female Brain Neoplasms Aged
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Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

The Lancet. Oncology Apr 2, 2019
BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly re...
Female Reproducibility of Results Brain Neoplasms Male Humans Neural Networks, Computer Databases, Factual Retrospective Studies Magnetic Resonance Imaging Multicenter Studies as Topic Randomized Controlled Trials as Topic Clinical Trials, Phase II as Topic Disease Progression Diagnosis, Computer-Assisted Time Factors Treatment Outcome Predictive Value of Tests Automation Workflow Germany Tumor Burden Clinical Trials, Phase III as Topic Image Interpretation, Computer-Assisted
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Improving Automatic Polyp Detection Using CNN by Exploiting Temporal Dependency in Colonoscopy Video.

IEEE journal of biomedical and health informatics Apr 1, 2019
Automatic polyp detection has been shown to be difficult due to various polyp-like structures in the colon and high interclass variations in polyp size, color, shape, and texture. An efficient method should not only have a high correct detection rate...
Colonoscopy Colonic Polyps Video Recording Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Algorithms
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Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

European radiology Mar 29, 2019
OBJECTIVES: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).
Adult Breast Neoplasms Middle Aged ROC Curve Retrospective Studies Ultrasonography, Mammary Humans Clinical Decision-Making Image Interpretation, Computer-Assisted Neural Networks, Computer Female Observer Variation Algorithms Aged
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Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury.

AJNR. American journal of neuroradiology Mar 28, 2019
BACKGROUND AND PURPOSE: Our aim was to use 2D convolutional neural networks for automatic segmentation of the spinal cord and traumatic contusion injury from axial T2-weighted MR imaging in a cohort of patients with acute spinal cord injury.
Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Spinal Cord Injuries Motor Disorders Humans Female Male Contusions Deep Learning Image Processing, Computer-Assisted
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