Machine learning is an avenue of computer science that can extrapolate information based on observed patterns without explicit programming.The defining characteristic of machine learning programs is the improved performance when more data, known as training data, is processed.
Machine learning is becoming an increasingly important tool in the medical profession as a primary computer-aided diagnosis algorithm or a decision support system. Interest in the practical applications of machine learning, including imaging, is high and growing rapidly, driven by the availability of large scale datasets, substantial advances in computing power, and new deep-learning algorithms. It is highly likely that in the next 10-20 years various implementations of machine learning will have a very profound impact on the way radiology is practised and it seems, at least to many in the field, inevitable that many of the tasks that are currently considered core to the practice of radiology (e.g. abnormality detection and classification) will be performed at least in part by these systems. It is therefore prudent that radiologists become familiar with the fundamentals of these approaches.
Although there are countless specific models and implementations of machine learning, the majority used in radiology fall into one of a relatively small number of fundamentally different underlying learning processes and models.
How the aforementioned learning processes are implemented is variable and determined in part by the type of problem being solved. Although much of the recent work in the field of image processing generally, and more specifically radiology, has focused on convolutional neural networks, a type of neural network, a number of other models are useful in various circumstances. These include:
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