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Video-based automatic hand hygiene detection for operating rooms using 3D convolutional neural networks
Hand hygiene among anesthesia personnel is important to prevent hospital-acquired infections in operating rooms; however, an efficient monitoring...
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Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter
Lung cancer creates pulmonary nodules in the patient’s lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated...
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FAST skill assessment from kinematics data using convolutional neural networks
PurposeFAST is a point of care ultrasound study that evaluates for the presence of free fluid, typically hemoperitoneum in trauma patients. FAST is...
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An Automated Heart Shunt Recognition Pipeline Using Deep Neural Networks
Automated recognition of heart shunts using saline contrast transthoracic echocardiography (SC-TTE) has the potential to transform clinical practice,...
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Automated detection of IVC filters on radiographs with deep convolutional neural networks
PurposeTo create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen...
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Letter to the Editor Regarding Article “Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset”
The cited article reports on a convolutional neural network trained to predict response to neoadjuvant chemotherapy from pre-treatment breast MRI...
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Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity
BackgroundFunctional near-infrared spectroscopy (fNIRS) is a tool to assess brain activity during cognitive testing. Despite its usefulness, its...
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Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks
Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the...
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Region-Based Semi-Two-Stream Convolutional Neural Networks for Pressure Ulcer Recognition
Pressure ulcers are a common, painful, costly, and often preventable complication associated with prolonged immobility in bedridden patients. It is a...
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Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery
Purpose:Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different...
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Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network
Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network,...
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An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network
Orthodontically induced external root resorption (OIERR) is a common complication of orthodontic treatments. Accurate OIERR grading is crucial for...
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The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks
We aimed to develop and validate a deep convolutional neural network (DCNN) model capable of accurately identifying spondylolysis or...
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Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images
BackgroundA deep convolutional neural network (DCNN) system is proposed to measure the lower limb parameters of the mechanical lateral distal femur...
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Automated detection of myopic maculopathy from color fundus photographs using deep convolutional neural networks
BackgroundMyopic maculopathy (MM) has become a major cause of visual impairment and blindness worldwide, especially in East Asian countries. Deep...
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Automatic 3D Segmentation and Identification of Anomalous Aortic Origin of the Coronary Arteries Combining Multi-view 2D Convolutional Neural Networks
This work aimed to automatically segment and classify the coronary arteries with either normal or anomalous origin from the aorta (AAOCA) using...
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Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN)
BackgroundNuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the...
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Influence of Data Augmentation Strategies on the Segmentation of Oral Histological Images Using Fully Convolutional Neural Networks
Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including...
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Abdominal fat quantification using convolutional networks
ObjectivesTo present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully...
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Semantic characteristic grading of pulmonary nodules based on deep neural networks
BackgroundAccurate grading of semantic characteristics is helpful for radiologists to determine the probabilities of the likelihood of malignancy of...
