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Japanese Journal of Radiology

, Volume 37, Issue 1, pp 15–33 | Cite as

Technical and clinical overview of deep learning in radiology

  • Daiju UedaEmail author
  • Akitoshi Shimazaki
  • Yukio Miki
Invited Review

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.

Keywords

Deep learning Artificial intelligence AI Neural network Radiology Review 

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

Notes

Funding

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

Compliance with ethical standards

Conflict of interest

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

Ethical considerations

This article does not contain any research involving human participants or animals performed by any of the authors.

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Copyright information

© Japan Radiological Society 2018

Authors and Affiliations

  1. 1.Department of Diagnostic and Interventional RadiologyOsaka City University Graduate School of MedicineOsakaJapan

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