Machine Learning for Medical Imaging

By Bradley James Erickson, P. Korfiatis, Zeynettin Akkus, T. Kline. 2017

1,023 citations recorded by the source corpus.

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [102]27, covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [103, 104, 105, 106, 107, 108, 109, 110, 111, 1”
Hello World Deep Learning in Medical Imaging (2018, methodology): “More information regarding principles of splitting and evaluating your model, including more robust methodologies such as cross-validation, are referenced here [8, 9].”
A Review on a Deep Learning Perspective in Brain Cancer Classification (2019, methodology): “In the case of brain image analysis, ML has been used in characterizing brain tumors [75,76].”
Integrating model simulation tools and cryo‐electron microscopy (2022, methodology): “97,98 Given that machine learning is a proven area of study for various pattern recognition problems, such as object detection in images, 99,100 it has been successfully applied to identifying SSEs from cryo-EM maps, and recently applied also to higher resolution maps.”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, background): “The field of machine learning is very diverse and has already had notable applications in medical imaging.(20) Deep learning is a subdiscipline of machine learning that relies on networks of simple interconnected units.”
A Gentle Introduction to Deep Learning in Medical Image Processing (2018, background): “tutorials and reviews [9, 10, 11, 12, 13, 14, 15, 16, 17, 18] over blog posts and jupyter notebooks [19, 20, 21] to entire books [22, 23].”
Introducing Artificial Intelligence Training in Medical Education (2019, neutral): “Examples of advances in AI include natural language processing (NLP) [16], speech recognition [17], virtual agents [18], decision management [19], machine learning [20], deep learning [21], and robotic process automation [22].”
Artificial intelligence and machine learning for medical imaging: A technology review. (2021, neutral): “Classification is the other prominent task in ML. Classifiers are simply algorithms that can sort data into groups or categories, and there exists a large variety of them [123].”