AIMC Topic:
Image Interpretation, Computer-Assisted

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Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.

Computational and mathematical methods in medicine May 5, 2020
Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper,...
Middle Aged Computational Biology Contrast Media Magnetic Resonance Imaging Breast Humans Image Interpretation, Computer-Assisted Aged Neural Networks, Computer Young Adult Databases, Factual Adult Female Breast Neoplasms
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Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization.

Journal of vascular and interventional radiology : JVIR May 4, 2020
PURPOSE: To develop and validate a deep learning model based on routine magnetic resonance (MR) imaging obtained before uterine fibroid embolization to predict procedure outcome.
Humans Retrospective Studies Clinical Decision-Making Middle Aged Diagnosis, Computer-Assisted Deep Learning Treatment Outcome Magnetic Resonance Imaging Female Reproducibility of Results Aged Adult Philadelphia Leiomyoma Uterine Artery Embolization Predictive Value of Tests Uterine Neoplasms Image Interpretation, Computer-Assisted Observer Variation
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Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial.

Gastroenterology May 1, 2020
BACKGROUND & AIMS: One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. ...
Mass Screening Adenoma Biopsy Image Interpretation, Computer-Assisted Female Aged Humans Middle Aged Colonoscopy Colorectal Neoplasms Colon Deep Learning Male Time Factors
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Segmenting nailfold capillaries using an improved U-net network.

Microvascular research May 1, 2020
To assess the microcirculation in a patient's capillaries, clinicians often use the valuable and non-invasive diagnostic tool of nailfold capillaroscopy (NC). In particular, evaluating the images that result from NC is particularly important for diag...
Humans Neural Networks, Computer Female Reproducibility of Results Predictive Value of Tests Image Interpretation, Computer-Assisted Nails Microscopic Angioscopy Capillaries Young Adult Adult Male Middle Aged
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Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.

European radiology Apr 29, 2020
OBJECTIVES: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.
Humans Neural Networks, Computer Angiomyolipoma Adenoma, Oxyphilic Machine Learning Contrast Media Image Interpretation, Computer-Assisted Algorithms Kidney Neoplasms Diagnosis, Differential Retrospective Studies Carcinoma, Renal Cell Tomography, X-Ray Computed
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Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.

The Laryngoscope Apr 28, 2020
OBJECTIVES/HYPOTHESIS: Create an autonomous computational system to classify endoscopy findings.
Laryngoscopy Laryngeal Neoplasms Laryngeal Diseases Larynx Artificial Intelligence Machine Learning Biopsy Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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Validation of machine learning models to detect amyloid pathologies across institutions.

Acta neuropathologica communications Apr 28, 2020
Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning ha...
Lewy Body Disease TDP-43 Proteinopathies Amyloid beta-Peptides Neurodegenerative Diseases Alzheimer Disease Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Machine Learning
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Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology Apr 27, 2020
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more tr...
False Negative Reactions Pandemics False Positive Reactions Reverse Transcriptase Polymerase Chain Reaction SARS-CoV-2 COVID-19 Clinical Laboratory Techniques Coronavirus Infections Tomography, X-Ray Computed Pneumonia, Viral Sensitivity and Specificity Betacoronavirus COVID-19 Testing Humans Lung Image Interpretation, Computer-Assisted Neural Networks, Computer Deep Learning
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A convolutional neural network to detect scoliosis treatment in radiographs.

International journal of computer assisted radiology and surgery Apr 26, 2020
PURPOSE: The aim of this work is to propose a classification algorithm to automatically detect treatment for scoliosis (brace, implant or no treatment) in postero-anterior radiographs. Such automatic labelling of radiographs could represent a step to...
Image Interpretation, Computer-Assisted Neural Networks, Computer Adolescent Algorithms Scoliosis Humans Databases, Factual Braces Radiography Prostheses and Implants Spine
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Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia Apr 23, 2020
Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologi...
Infratentorial Neoplasms Diagnosis, Differential Image Interpretation, Computer-Assisted Astrocytoma Ependymoma Machine Learning Female Infant, Newborn Male Humans Child, Preschool Child Magnetic Resonance Imaging Infant Adolescent
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