A Gentle Introduction to Deep Learning in Medical Image Processing

By Andreas K. Maier, Christopher Syben, Tobias Lasser, Christian Riess. Zeitschrift für Medizinische Physik, 2018

467 citations recorded by the source corpus.

This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep ()learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b0d2ec75395f99c4b4c19c62af1b572384701be6

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Citations in the corpus

The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis (2020, background): “net/N2V), object and pattern recognition, and segmentation.(93) DeepImageJ (https://deepimagej.”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “…has produced satisfactory results in most of the cases, However, in a sensitive ﬁeld like medical image processing prior knowledge should be incorporated in cases of image detection and recognition, reconstruction so that the data driven approaches do not produce implausible results [219].”
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].”
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”
Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning (2020, background): “DL improves such a medical system to realize higher outcomes, widen illness scope, and implementing applicable real-time medical image [19,20] disease detection systems.”