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Technical and clinical overview of deep learning in radiology

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Abstract

Deep learning has been applied to clinical applications in not only radiology, but also all other areas of medicine. This review provides a technical and clinical overview of deep learning in radiology. To gain a more practical understanding of deep learning, deep learning techniques are divided into five categories: classification, object detection, semantic segmentation, image processing, and natural language processing. After a brief overview of technical network evolutions, clinical applications based on deep learning are introduced. The clinical applications are then summarized to reveal the features of deep learning, which are highly dependent on training and test datasets. The core technology in deep learning is developed by image classification tasks. In the medical field, radiologists are specialists in such tasks. Using clinical applications based on deep learning would, therefore, be expected to contribute to substantial improvements in radiology. By gaining a better understanding of the features of deep learning, radiologists could be expected to lead medical development.

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Abbreviations

NLP:

Natural language processing

ANN:

Artificial neural network

AUC:

Area under the curve

ROC:

Receiver operating characteristic

CNN:

Convolutional neural network

SR:

Super resolution

LR:

Low resolution

HR:

High resolution

GAN:

Generative adversarial networks

NAS:

Neural architecture search

ILSVRC:

ImageNet large-scale visual recognition challenge

FCN:

Fully convolutional network

CRF:

Conditional random field

R-CNN:

Regions with convolutional neural network features

YOLO:

You only look once

SSD:

Single shot MultiBox detector

PSP:

Pyramid scene parsing

FSRCNN:

Fast super resolution convolutional neural network

ESPCN:

Efficient sub-pixel convolutional neural network

VDSR:

Very deep super resolution

DRCN:

Deeply-recursive convolutional network

EDSR:

Enhanced deep super resolution network

RDN:

Residual dense network

DBPN:

Deep back-projection networks

ZSSR:

Zero shot super resolution

CBOW:

Continuous bag-of-words

GloVe:

Global vectors for word representation

DCGAN:

Deep convolutional generative adversarial network

XOGAN:

Generative adversarial network with XO-structure

ENAS:

Efficient neural architecture search

DARTS:

Differentiable architecture search

NAO:

Neural architecture optimization

HMH:

Hemorrhage, mass effect, or hydrocephalus

CT:

Computed tomography

SAI:

Suspected acute infarct

HCC:

Hepato-cellular carcinoma

MR:

Magnetic resonance

MCI:

Mild cognitive impairment

ICH:

Intracranial hemorrhage

EDH/SDH:

Epidural/subdural hemorrhage

SAH:

Subarachnoid hemorrhage

ASL:

Arterial spin labeling

VN:

Variational network

PICS:

Parallel imaging and compressed sensing

DnCNN:

Denoising convolutional neural network

PE:

Pulmonary embolism

AI:

Artificial intelligence

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Funding

Another research about a deep learning for mammography received 10,000$ in 2017 from Wellness Open Living Labs. LLC, Osaka, Japan.

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  1. Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan

    Daiju Ueda, Akitoshi Shimazaki & Yukio Miki

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  1. Daiju Ueda
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  2. Akitoshi Shimazaki
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Correspondence to Daiju Ueda.

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Daiju Ueda received a research grant from Wellness Open Living Labs, LLC.

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Ueda, D., Shimazaki, A. & Miki, Y. Technical and clinical overview of deep learning in radiology. Jpn J Radiol 37, 15–33 (2019). https://doi.org/10.1007/s11604-018-0795-3

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