Deep Learning in Medical Imaging: General Overview

By June-Goo Lee, Sanghoon Jun, Younghoon Cho, Hyunna Lee, Guk Bae Kim, Joon Beom Seo, Namkug Kim. 2017

1,168 citations recorded by the source corpus.

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Real-world data: a brief review of the methods, applications, challenges and opportunities (2022, methodology): “For example, ML is widely applied in in health informatics to generate RWE and formulate personalized healthcare [86–90] and was successfully employed on RWD collected during the COVID-19 pandemic to help understand the disease and evaluate its prevention and treatment strategies [91–95].”
Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers (2019, background): “Records excluded (n = 333): Review articles (14) Editorials/letters/comments (1) Conference abstracts/proceedings (1) Not in field of interest (270) Non-human subject research (24) Unable to obtain full-text article (23) Full-text articles assessed for eligibility (n = 849)”
Machine learning and deep learning approach for medical image analysis: diagnosis to detection (2022, neutral): “A brief introduction of DNN in medical image analysis to diagnose the brain tumor using brain tissues is provided in [56].”
Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018, methodology): “Radiologists are already familiar with computer-aided detection/diagnosis (CAD) systems, which were first introduced in the 1960s in chest x-ray and mammography applications [5].”
Artificial intelligence in drug development: present status and future prospects. (2019, background): “AI also contains a subﬁeld called machine learning (ML), which uses statistical meth-ods with the ability to learn with or without being explicitly programmed [1–3].”
Artificial intelligence in radiology (2018, neutral): “There is great debate about the speed with which newer deep learning methods will be implemented in clinical radiology practice 88 , with speculations for the time needed to fully automate clinical tasks ranging from a few years to decades.”
Artificial Intelligence in Cardiovascular Imaging (2019, methodology): “Fully automated diagnostic tools will ultimately provide medical opinions (12,26), but will also require extensive validation before regulatory approval.”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, positive): “…based methods have provided state-of-the-art performance for various healthcare tasks including medical image reconstruction [1], management of electronic health records [2], cancer segmentation [3], disease prediction [4], clinical imaging [5], image retrieval [6], and computational biology [7].”
Current Applications and Future Impact of Machine Learning in Radiology. (2018, neutral): “Also, the validation process must be highly robust, otherwise the algorithm could be subject to overfitting to a particular subclassification of data (80).”