Deep Learning Applications in Medical Image Analysis

By Justin Ker, Lipo Wang, J. Rao, Tchoyoson C. C. Lim. 2018

1,242 citations recorded by the source corpus.

The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field. The advantage of machine learning in an era of medical big data is that significant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classification, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “On the other hand, in just a few years, breakthroughs in ML and particularly deep learning have transformed every aspect of our lives from face recognition [6], [7], medical diagnosis [8], [9], and natural language processing (NLP) [10], [11].”
Deep Learning in Medical Imaging (2019, background): “54 Medical images are mostly imbalanced, and time-consuming to obtain their labels.”
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Deep learning in medical image registration: a survey (2019, background): “learning in medical image registration has not been extensively studied until the past three to four years. Though several review papers on deep learning in medical image analysis have been published [73,93,96,105,106,121,132,182], there are very few review papers that are speciﬁc to deep learning i”
Feature Learning and Analysis for Cleanliness Classification in Restrooms (2019, methodology): “With the recent development in CNN, it has been commonly applied in diverse real-world applications such as medical image analysis [18], time series analysis [19], speech recognition [20], etc.”
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].”
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Deep learning in medical image registration: a review (2019, background): “Though several review papers on deep learning in medical image analysis have been published [73, 93, 96, 105, 106, 121, 132, 182], there are very few review papers that are specific to deep learning in medical image registration [60].”
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Februus: Input Purification Defence Against Trojan Attacks on Deep Neural Network Systems (2019, background): “Consequently, deep neural network (DNN) systems are increasingly entrusted to make critical decisions on our behalf in self-driving cars [6], disease diagnosis [15], facial recognition [22, 23], malware detection [25] and so on.”
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