Over the past decade, rapid development in biological and chemical technologies such as high-throughput screening, parallel synthesis, has been significantly increased the amount of data, which requires the creation and the integration of new analyti...
The Journal of the Acoustical Society of America
Sep 1, 2020
The performances of deep convolutional neural network (DCNN) modeling and transfer learning (TF) for thyroid tumor grading using ultrasound imaging were evaluated. This retrospective study included input patient data (ultrasound B-mode image sets) as...
SIGNIFICANCE: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining.
Generative models provide a well-established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural l...
BACKGROUND/AIM: Accumulating evidence has shown therapeutic effects of herbals on breast cancer, a commonly diagnosed malignancy in women worldwide. However, their underlying mechanisms remain unclear. We aimed to explore the mode of action of a rece...
Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete
Sep 1, 2020
BACKGROUND: Since 2017, there have been several reports of artificial intelligence (AI) achieving comparable performance to human experts on medical image analysis tasks. With the first ratification of a computer vision algorithm as a medical device ...
Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete
Sep 1, 2020
BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical practice. Especially in the image-based diagnosis of skin cancer, AI shows great potential. However, there is a significant discrepancy between expectations and true relev...
PURPOSE OF REVIEW: As artificial intelligence continues to develop new applications in ophthalmic image recognition, we provide here an introduction for ophthalmologists and a primer on the mechanisms of deep learning systems.
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