AIMC Topic: Image Interpretation, Computer-Assisted

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Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.

JCO clinical cancer informatics Feb 1, 2019
PURPOSE: Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and vali...
Molecular Imaging Image Interpretation, Computer-Assisted Radionuclide Imaging Parenchymal Tissue Mammography Breast Radiopharmaceuticals Humans Risk Factors Neural Networks, Computer Breast Neoplasms Algorithms Risk Assessment Female
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Network Tomography for Understanding Phenotypic Presentations in Aortic Stenosis.

JACC. Cardiovascular imaging Feb 1, 2019
OBJECTIVES: This study sought to build a patient-patient similarity network using multiple features of left ventricular (LV) structure and function in patients with aortic stenosis (AS). The study further validated the observations in an experimental...
Image Interpretation, Computer-Assisted Heart Valve Prosthesis Implantation Balloon Valvuloplasty Predictive Value of Tests Mice, Knockout Heart Ventricles Male Ventricular Function, Left Echocardiography Humans Severity of Illness Index Aged, 80 and over Disease Models, Animal Machine Learning Time Factors Cross-Sectional Studies Aortic Valve Stenosis Middle Aged Female Animals Aortic Valve Disease Progression Reproducibility of Results Aged
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Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map.

IEEE transactions on medical imaging Feb 1, 2019
The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the ...
Histological Techniques Deep Learning Image Interpretation, Computer-Assisted Cell Nucleus Humans Databases, Factual Neoplasms
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Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning.

Journal of digital imaging Feb 1, 2019
Breast cancer is a leading cause of cancer death among women in the USA. Screening mammography is effective in reducing mortality, but has a high rate of unnecessary recalls and biopsies. While deep learning can be applied to mammography, large-scale...
Humans Databases, Factual Middle Aged Deep Learning Mammography Breast Electronic Health Records Female Breast Neoplasms Image Interpretation, Computer-Assisted
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Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.

Journal of digital imaging Feb 1, 2019
The aim of this study is to develop a fully automated convolutional neural network (CNN) method for quantification of breast MRI fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). An institutional review board-approved retrospe...
Imaging, Three-Dimensional Magnetic Resonance Imaging Retrospective Studies Humans Image Interpretation, Computer-Assisted Breast Female Neural Networks, Computer Breast Neoplasms
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Tumor Identification in Colorectal Histology Images Using a Convolutional Neural Network.

Journal of digital imaging Feb 1, 2019
Colorectal cancer (CRC) is a major global health concern. Its early diagnosis is extremely important, as it determines treatment options and strongly influences the length of survival. Histologic diagnosis can be made by pathologists based on images ...
Neural Networks, Computer Image Interpretation, Computer-Assisted Republic of Korea Sensitivity and Specificity Humans Colorectal Neoplasms Diagnostic Imaging
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AdaptAhead Optimization Algorithm for Learning Deep CNN Applied to MRI Segmentation.

Journal of digital imaging Feb 1, 2019
Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low...
Humans Neural Networks, Computer Brain Neoplasms Algorithms Magnetic Resonance Imaging Deep Learning Brain Image Interpretation, Computer-Assisted Reproducibility of Results
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A Survey on Medical Image Analysis in Capsule Endoscopy.

Current medical imaging reviews Jan 1, 2019
BACKGROUND AND OBJECTIVE: Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detect...
Image Enhancement Humans Image Interpretation, Computer-Assisted Capsule Endoscopy
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Categorization & Recognition of Lung Tumor Using Machine Learning Representations.

Current medical imaging reviews Jan 1, 2019
BACKGROUND: Lung Cancer is the disease spreading around the world nowadays. Early recognition of lung disease is a difficult task as the cells which cause tumor will grow quickly and the majority of these cells are enclosed with each other. From the ...
Image Interpretation, Computer-Assisted Humans Algorithms Machine Learning Lung Neoplasms Early Detection of Cancer
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A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm.

Current medical imaging reviews Jan 1, 2019
BACKGROUND: The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum.
Humans Decision Trees Image Interpretation, Computer-Assisted Algorithms Machine Learning Diagnosis, Differential Image Enhancement Brain Brain Infarction Stroke Magnetic Resonance Imaging
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