AIMC Topic: Deep Learning

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A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine Jan 22, 2020
PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning ha...
Case-Control Studies Diagnosis, Computer-Assisted Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Aged, 80 and over Male Middle Aged Disease Progression Neuroimaging Hydrocephalus, Normal Pressure Magnetic Resonance Imaging Diagnosis, Differential Alzheimer Disease Pattern Recognition, Automated Humans Artificial Intelligence Brain Female Reproducibility of Results Aged
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Automated body composition analysis of clinically acquired computed tomography scans using neural networks.

Clinical nutrition (Edinburgh, Scotland) Jan 22, 2020
BACKGROUND & AIMS: The quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantificatio...
Predictive Value of Tests Body Composition Radiographic Image Interpretation, Computer-Assisted Lumbar Vertebrae Neural Networks, Computer Adiposity Automation Tomography, X-Ray Computed Europe North America Female Sarcopenia Reproducibility of Results Muscle, Skeletal Adipose Tissue Humans Aged Adult Male Middle Aged Deep Learning
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Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction.

NeuroImage Jan 22, 2020
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand ...
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Cerebral Hemorrhage Multiple Sclerosis Neuroimaging Humans Brain Computer Simulation Adult
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An improved deep network for tissue microstructure estimation with uncertainty quantification.

Medical image analysis Jan 22, 2020
Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients. These methods learn the mapping from diffusion signals in ...
Connectome Humans Uncertainty Deep Learning Diffusion Magnetic Resonance Imaging Image Enhancement White Matter
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Using Deep Learning in Ultrasound Imaging of Bicipital Peritendinous Effusion to Grade Inflammation Severity.

IEEE journal of biomedical and health informatics Jan 22, 2020
Inflammation of the long head of the biceps tendon is a common cause of shoulder pain. Bicipital peritendinous effusion (BPE) is the most common biceps tendon abnormality and is related to various shoulder injuries. Physicians usually use ultrasound ...
Tendons Shoulder Joint Shoulder Pain Muscular Diseases Deep Learning Ultrasonography Inflammation Muscle, Skeletal Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

The lancet. Gastroenterology & hepatology Jan 22, 2020
BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system...
Humans Mass Screening Female Adenoma Case-Control Studies Adult Double-Blind Method Diagnosis, Computer-Assisted Observer Variation Colonic Polyps Deep Learning Colorectal Neoplasms Early Diagnosis China Male Clinical Alarms Incidence Middle Aged Colonoscopy
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A deep convolutional neural network architecture for interstitial lung disease pattern classification.

Medical & biological engineering & computing Jan 22, 2020
Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity am...
Lung Diseases, Interstitial Humans Neural Networks, Computer Databases, Factual Tomography, X-Ray Computed Diagnosis, Computer-Assisted Deep Learning
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Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

AJR. American journal of roentgenology Jan 22, 2020
The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. Our prospective mult...
Humans Prospective Studies Tomography, X-Ray Computed Radiation Dosage Deep Learning Radiography, Thoracic Female Imaging, Three-Dimensional Radiography, Abdominal Aged Contrast Media Male Radiographic Image Interpretation, Computer-Assisted Middle Aged
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Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.

Journal of the European Academy of Dermatology and Venereology : JEADV Jan 21, 2020
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not be...
Skin Neoplasms Dermoscopy Aged Young Adult Adult Humans Female Melanoma Deep Learning Clinical Competence Dermatologists Melanocytes Nevus, Pigmented Sensitivity and Specificity Male Middle Aged Diagnosis, Computer-Assisted
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Myelin water imaging data analysis in less than one minute.

NeuroImage Jan 21, 2020
PURPOSE: Based on a deep learning neural network (NN) algorithm, a super fast and easy to implement data analysis method was proposed for myelin water imaging (MWI) to calculate the myelin water fraction (MWF).
Myelin Sheath Body Water Neuroimaging Deep Learning Magnetic Resonance Imaging Feasibility Studies Multiple Sclerosis Female Adult Male Middle Aged Humans Brain
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