While Machine Learning and Deep Learning (ML/DL) is often seen as a sub-category of Artificial Intelligence (AI), it would be correct to think of it as the current state-of-the-art of AI – ML is currently showing the greatest potential in providing tools to industries and societies to drive change. Machine Learning is an approach to AI showing great potential when it comes to developing autonomous, self-learning systems which are revolutionising and disrupting many industries.
In the simplest way possible, a Machine Learning algorithm is trained from a specified ‘training set’ of data which is then used as the basis to solve a given problem. An example of this would be when a computer is given a training set of photographs, some which say “this is a flower”, and some which indicate “this is not a flower.” Next you would show the computer a series of new images and it would start to distinguish which photos contain flowers.
Machine Learning continuously adds to its date set by identifying every picture (correctly or incorrectly), essentially becoming ‘smarter’ and more efficient at completing tasks over time. It is, in effect, learning.
Deep Learning (DL), on the other hand, can be considered as the ‘cutting-edge of the cutting-edge.’ Essentially, DL fuses AI’s core ideas and revolves them around solving real-world problems with deep neural-networks designed to mimic our own decision-making. It is easiest to describe Deep Learning as a sub-set which focuses on narrower tools and techniques, applying them to solve just about any problem which requires ‘thought’ – human or artificial.
Computer Vision is reaching an advanced stage, and is being used across daily life and business operations to conduct multiple functions such as face recognition, identification, verification, emotion analysis, and crowd analytics. Further uses include: warning drivers of animals on the road and pinpointing issues in x-rays.
Gaming has also seen Computer Vision being integrated into its systems such as the Xbox Kinect and PlayStation Eye which “sees” and analyses our movement. Evidently, Computer Vision is being implemented into our everyday lives to enhance the entertainment industry, security, autonomy and provide us with invaluable statistics.
Computer Vision is now being used to scan social media platforms to find relevant images which would not be available through traditional searches. The technology itself is far more complex and intuitive, and just like the aforementioned tasks, it requires more than just image recognition, but also semantic big data and analysis.
It has taken computer scientists almost 80 years to get to where we are today and with Deep Learning, Data Modelling and Computer Vision, we are continuously refining the Artificial Intelligence landscape.
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AI can automatically diagnose enormous amount of CT and other medical images. Thus, by detecting abnormalities, AI can contribute to reduction of radiologists' workload.
In recent years, the need for diagnosis using medical images from CT and MRI has been increasing. However, in medical fields around the world including Japan, there is a shortage of radiologists who are image diagnostic specialists, despite the prevalence of the machines. Due to this, depending on the region, the difficulty in quickly providing advanced image diagnosis has become a social issue. Also, with recent sophistication in diagnostic imaging equipment, which has rapidly increased the number of images that can be taken in a single scan, the growing diagnostic workload of radiologists has become an issue.
On the other hand, image diagnosis is extremely effective for early detection of serious diseases such as cancer, heart disease and cerebrovascular disease. It is an indispensable diagnostic method in improved preventive medicine. Therefore, the meaning of streamlining the workflow from producing images with the machines to diagnosing the images and creating an AI solution which can support high quality image diagnosis by radiologists, making it penetrate the medical field, is extremely significant.