AIMC Topic: Deep Learning

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Deep Learning: Current and Emerging Applications in Medicine and Technology.

IEEE journal of biomedical and health informatics Jan 23, 2019
Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical ...
Drug Discovery Deep Learning Computational Biology Robotics Humans Drug Design Nanomedicine Image Interpretation, Computer-Assisted Biotechnology
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Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space.

Journal of chemical information and modeling Jan 23, 2019
Acute toxicity is one of the most challenging properties to predict purely with computational methods due to its direct relationship to biological interactions. Moreover, toxicity can be represented by different end points: it can be measured for dif...
Deep Learning Models, Theoretical Toxicity Tests, Acute Endpoint Determination Animals
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A deep learning method to more accurately recall known lysine acetylation sites.

BMC bioinformatics Jan 23, 2019
BACKGROUND: Lysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding o...
Acetylation Deep Learning Lysine Protein Processing, Post-Translational
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Early esophageal adenocarcinoma detection using deep learning methods.

International journal of computer assisted radiology and surgery Jan 22, 2019
PURPOSE: This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) ...
Humans Deep Learning Neural Networks, Computer Adenocarcinoma Reproducibility of Results Early Diagnosis Esophageal Neoplasms
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Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence.

Biomedical engineering online Jan 22, 2019
BACKGROUND: A deep learning computer artificial intelligence system is helpful for early identification of ground glass opacities (GGOs).
Radiographic Image Interpretation, Computer-Assisted False Positive Reactions Lung Neoplasms Solitary Pulmonary Nodule Radiology Databases, Factual Tomography, X-Ray Computed Reproducibility of Results Sensitivity and Specificity Lung Diagnosis, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Humans Algorithms
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Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images.

NeuroImage. Clinical Jan 22, 2019
We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detecti...
Deep Learning Epilepsies, Partial Humans Adult Adolescent Female Sensitivity and Specificity Child Image Processing, Computer-Assisted Young Adult Male Electroencephalography Middle Aged Seizures Scalp
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Parallel imaging in time-of-flight magnetic resonance angiography using deep multistream convolutional neural networks.

Magnetic resonance in medicine Jan 21, 2019
PURPOSE: To develop and evaluate a method of parallel imaging time-of-flight (TOF) MRA using deep multistream convolutional neural networks (CNNs).
Brain Humans Deep Learning Algorithms Magnetic Resonance Angiography Cerebral Angiography Image Processing, Computer-Assisted
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Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Magnetic resonance in medicine Jan 21, 2019
PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectr...
Brain Neoplasms Deep Learning User-Computer Interface Image Processing, Computer-Assisted Software Algorithms Databases, Factual Artifacts Signal-To-Noise Ratio White Matter Magnetic Resonance Spectroscopy Models, Theoretical Brain Mapping Glioblastoma Choline Humans Neural Networks, Computer Echo-Planar Imaging Aspartic Acid Computer Graphics Creatine
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Objective auditory brainstem response classification using machine learning.

International journal of audiology Jan 21, 2019
OBJECTIVE: The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: 'clear response', 'inconclusive' or 'response absent'.
Deep Learning Signal Processing, Computer-Assisted Acoustic Stimulation Hearing Tests Young Adult Adult Male Auditory Threshold Humans Evoked Potentials, Auditory, Brain Stem Databases, Factual Brain Stem Female
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Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.

EBioMedicine Jan 20, 2019
BACKGROUND: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel ...
Male Middle Aged Deep Learning Adolescent ROC Curve Skin Sound Aged Aged, 80 and over Humans Telemedicine Young Adult Artificial Intelligence Adult Female Algorithms Skin Neoplasms Retrospective Studies Dermoscopy
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