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Medical imaging (such as X-ray and photography) is a commonly used tool in dermatology [54] and the development of deep learning has been strongly tied to image processing. Therefore, there is a natural fit between the dermatology and deep learning. Machine learning learning holds great potential to process these images for better diagnoses. [55]
HRHIS is a human resource for health information system for management of human resources for health developed by University of Dar es Salaam college of information and communication technology, Department of Computer Science and Engineering, for Ministry of Health and Social Welfare (Tanzania) and funded by the Japan International Cooperation ...
The MIT Abdul Latif Jameel Clinic for Machine Learning in Health (commonly, MIT Jameel Clinic; previously, J-Clinic) is a research center at the Massachusetts Institute of Technology (MIT) in the field of artificial intelligence (AI) and health sciences, including disease detection, drug discovery, and the development of medical devices.
Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics, Biomedical informatics, and machine learning, with the goal of understanding biological and medical data.
Pages in category "Deep learning software applications" The following 31 pages are in this category, out of 31 total. This list may not reflect recent changes.
MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks: 1992 Proprietary: No Linux, macOS, Windows: C, C++, Java, MATLAB: MATLAB: No No Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder [23] No Yes [24] Yes [25] [26] Yes [25] Yes [25] Yes With Parallel Computing Toolbox [27] Yes Microsoft Cognitive ...
GNoME employs deep learning techniques to efficiently explore potential material structures, achieving a significant increase in the identification of stable inorganic crystal structures. The system's predictions were validated through autonomous robotic experiments, demonstrating a noteworthy success rate of 71%.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.