The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has re...
Computational and mathematical methods in medicine
Apr 9, 2020
In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regu...
Wrong-site surgeries can occur due to the absence of an appropriate surgical time-out. However, during a time-out, surgical participants are unable to review the patient's charts due to their aseptic hands. To improve the conditions in surgical time-...
Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis. However, a low-quality CMR (cardiac magnetic resonance) image with a large amount of noise makes it...
PURPOSE: To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) using deep learning (DL) and investigate the association with local and global growth ...
International journal of computer assisted radiology and surgery
Apr 8, 2020
PURPOSE: Deep learning has recently shown its outstanding performance in biomedical image semantic segmentation. Most biomedical semantic segmentation frameworks comprise the encoder-decoder architecture directly fusing features of the encoder and th...
Deep learning approaches, especially convolutional neural network (CNN) models, have achieved excellent performances in vibrational spectral analysis. The critical drawback of the CNN approach is the lack of interpretation, and it is regarded as a bl...
Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural n...
Geriatrics & gerontology international
Apr 8, 2020
AIM: This study aimed to use a convolutional neural network (CNN) to investigate the associations between the time of falling and multiple complicating factors, including age, dementia severity, lower extremity strength and physical function, among n...
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