Machine Learning and Radiology

By Shijun Wang, Ronald M. Summers. Medical Image Anal., 2012

660 citations recorded by the source corpus.

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.

Subfields: Artificial Intelligence, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/222c68fc80b05064680ddc467243b620604fd11e

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In Artificial Intelligence

In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “sses in CT [48], and in the identication of multiple sclerosis lesions in MRI [49]. Apart from the classication of radiological images, analysis of radiological text reports plays a signicant role [50]. The most prominent approach in this type of classication is deep learningbased natural language p”
Automated detection and classification of the proximal humerus fracture by using deep learning algorithm (2018, background): “Thus , a more efﬁ cient and accurate manner of diagnosing and classifying fracture (Wang and Summers 2012, Bengio et al. 2013, LeCun et al. 2015).”
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks (2019, background): “Bayesian approaches to uncertainty estimation have indeed been proposed to assess the reliability of clinical predictions [24, 25, 47, 30] but have only been applied to a handful of large-scale real-world problems [30, 15, 42] that neural networks (NNs) have proven themselves particularly effective ”
eDoctor: machine learning and the future of medicine (2018, methodology): “ML techniques are growing in radiology, however, as a speciality that deals almost exclusively with data in the form of predominantly digital requests, images and linked text reports, it is a speciality that is ripe for ML [22].”
Toolkits and Libraries for Deep Learning (2017, neutral): “In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification [5 – 8].”
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation (2021, methodology): “Later approaches have focused on applying and adapting traditional machine learning techniques (Rundo et al., 2020b), such as support vector machines (SVMs) (Wang and Summers, 2012), unsupervised clustering (Ren et al., 2019) and atlas-based segmentation (Wachinger and Gol-land, 2014).”