Abstract
Recent fervor surrounding the use of artificial intelligence (AI) for radiology applications has been driven by the use of convolutional neural networks to greatly improve performance on a variety of imaging tasks. In this chapter, we give an overview of how to build an AI algorithm for imaging purposes with a focus on convolutional neural networks. We will discuss common types of imaging tasks, basic convolutional neural network components, common neural network architectures used for each type of imaging task, loss functions, and basics of training. We also discuss several more advanced concepts of algorithm design in addition to selection of deep learning libraries and hardware.
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Notes
- 1.
Also called a feature map.
- 2.
While this is the original formulation of a convolutional layer, in many cases, a padding operation is now performed, for instance, by adding zeros on the edges of the input images, so that the output has the same dimensions as the input.
- 3.
This is known as a regression problem, in which the output is a number rather than a class.
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Hahn, L., Masutani, E., Hasenstab, K. (2022). How to Build Artificial Intelligence Algorithms for Imaging Applications. In: De Cecco, C.N., van Assen, M., Leiner, T. (eds) Artificial Intelligence in Cardiothoracic Imaging. Contemporary Medical Imaging. Humana, Cham. https://doi.org/10.1007/978-3-030-92087-6_6
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