For many technology-driven industries, the constant breakthrough of technology is their necessary living conditions. However, in other areas, due to regulatory restrictions, they are more inclined to follow traditional practices. The medical field belongs to the latter. Although the implementation of the precision medical plan and the wave of contemporary mobile medical care have brought great progress to the industry, medical care, as an important part of the national economy, is still more conservative under various constraints, and it has some advanced potential. Tool acceptance is low.
Deep learning is not a new topic, and its application scenarios are still widely expanding. Traditional deep learning techniques are likely to have some more mature variants (convolution and recursive neural networks are their initial forms), providing specific functions for some medical fields and tapping into more possibilities. Of course, we will soon face a series of challenges.
In the past year, deep learning has shifted more from the commercial fields such as video, video and speech recognition analysis. For medical treatment, deep learning will be more deeply embedded in medical imaging, sensor-based data analysis, transformation of bioinformatics, and public health policy development.
A group of professional deep learning researchers specially analyzed the application of deep learning in medical information in recent years. It pointed out that the improvement of computer function, fast data storage and parallel computing is the basis for the rapid development of deep learning, and the depth. The predictive function of learning and automatic recognition make it popular in disease diagnosis. In addition, they also found that the use of deep learning technology in the medical industry not only has a significant increase in frequency, but also a certain change in the types. As one of the most important types of recurrent neural networks, it has decreased from nearly 90% in 2010 to 40% in 2015; while other improved types are used more and more frequently.
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