Literature DB >> 29498017

Deep learning with convolutional neural network in radiology.

Koichiro Yasaka1, Hiroyuki Akai2, Akira Kunimatsu2, Shigeru Kiryu3, Osamu Abe4.   

Abstract

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Keywords:  CT; Convolutional neural network; Deep learning; MRI; PET

Mesh:

Year:  2018        PMID: 29498017     DOI: 10.1007/s11604-018-0726-3

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN: 1867-1071            Impact factor:   2.374


  57 in total

1.  CT Image-based Decision Support System for Categorization of Liver Metastases Into Primary Cancer Sites: Initial Results.

Authors:  Avi Ben-Cohen; Eyal Klang; Idit Diamant; Noa Rozendorn; Stephen P Raskin; Eli Konen; Michal Marianne Amitai; Hayit Greenspan
Journal:  Acad Radiol       Date:  2017-08-01       Impact factor: 3.173

2.  Full and hybrid iterative reconstruction to reduce artifacts in abdominal CT for patients scanned without arm elevation.

Authors:  Koichiro Yasaka; Toshihiro Furuta; Takatoshi Kubo; Eriko Maeda; Masaki Katsura; Jiro Sato; Kuni Ohtomo
Journal:  Acta Radiol       Date:  2017-01-09       Impact factor: 1.990

3.  Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.

Authors:  Luciano M Prevedello; Barbaros S Erdal; John L Ryu; Kevin J Little; Mutlu Demirer; Songyue Qian; Richard D White
Journal:  Radiology       Date:  2017-07-03       Impact factor: 11.105

4.  Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin.

Authors:  Mohsen Ghafoorian; Nico Karssemeijer; Tom Heskes; Mayra Bergkamp; Joost Wissink; Jiri Obels; Karlijn Keizer; Frank-Erik de Leeuw; Bram van Ginneken; Elena Marchiori; Bram Platel
Journal:  Neuroimage Clin       Date:  2017-02-04       Impact factor: 4.881

5.  Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

Authors:  Fang Liu; Hyungseok Jang; Richard Kijowski; Tyler Bradshaw; Alan B McMillan
Journal:  Radiology       Date:  2017-09-19       Impact factor: 11.105

6.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival.

Authors:  Francesca Ng; Balaji Ganeshan; Robert Kozarski; Kenneth A Miles; Vicky Goh
Journal:  Radiology       Date:  2012-11-14       Impact factor: 11.105

7.  Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.

Authors:  Bulat Ibragimov; Diego Toesca; Daniel Chang; Albert Koong; Lei Xing
Journal:  Phys Med Biol       Date:  2017-11-10       Impact factor: 3.609

8.  A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Authors:  Jiangwei Lao; Yinsheng Chen; Zhi-Cheng Li; Qihua Li; Ji Zhang; Jing Liu; Guangtao Zhai
Journal:  Sci Rep       Date:  2017-09-04       Impact factor: 4.379

9.  Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

Authors:  Xinggang Wang; Wei Yang; Jeffrey Weinreb; Juan Han; Qiubai Li; Xiangchuang Kong; Yongluan Yan; Zan Ke; Bo Luo; Tao Liu; Liang Wang
Journal:  Sci Rep       Date:  2017-11-13       Impact factor: 4.379

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  50 in total

Review 1.  Current applications and future directions of deep learning in musculoskeletal radiology.

Authors:  Pauley Chea; Jacob C Mandell
Journal:  Skeletal Radiol       Date:  2019-08-04       Impact factor: 2.199

Review 2.  Technical and clinical overview of deep learning in radiology.

Authors:  Daiju Ueda; Akitoshi Shimazaki; Yukio Miki
Journal:  Jpn J Radiol       Date:  2018-12-01       Impact factor: 2.374

Review 3.  Improvement of image quality at CT and MRI using deep learning.

Authors:  Toru Higaki; Yuko Nakamura; Fuminari Tatsugami; Takeshi Nakaura; Kazuo Awai
Journal:  Jpn J Radiol       Date:  2018-11-29       Impact factor: 2.374

4.  Artificial intelligence in orthodontics : Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network.

Authors:  Felix Kunz; Angelika Stellzig-Eisenhauer; Florian Zeman; Julian Boldt
Journal:  J Orofac Orthop       Date:  2019-12-18       Impact factor: 1.938

5.  Visualizing "featureless" regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiology.

Authors:  Daiju Ueda; Akira Yamamoto; Tsutomu Takashima; Naoyoshi Onoda; Satoru Noda; Shinichiro Kashiwagi; Tamami Morisaki; Shinichi Tsutsumi; Takashi Honjo; Akitoshi Shimazaki; Takuya Goto; Yukio Miki
Journal:  Jpn J Radiol       Date:  2020-11-16       Impact factor: 2.374

6.  Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.

Authors:  Shigeru Kiryu; Koichiro Yasaka; Hiroyuki Akai; Yasuhiro Nakata; Yusuke Sugomori; Seigo Hara; Maria Seo; Osamu Abe; Kuni Ohtomo
Journal:  Eur Radiol       Date:  2019-07-01       Impact factor: 5.315

Review 7.  Updates on Imaging of Liver Tumors.

Authors:  Arya Haj-Mirzaian; Ana Kadivar; Ihab R Kamel; Atif Zaheer
Journal:  Curr Oncol Rep       Date:  2020-04-16       Impact factor: 5.075

8.  Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?

Authors:  Akira Yamada; Kazuki Oyama; Sachie Fujita; Eriko Yoshizawa; Fumihito Ichinohe; Daisuke Komatsu; Yasunari Fujinaga
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-05-03       Impact factor: 2.924

9.  Deep learning for staging liver fibrosis on CT: a pilot study.

Authors:  Koichiro Yasaka; Hiroyuki Akai; Akira Kunimatsu; Osamu Abe; Shigeru Kiryu
Journal:  Eur Radiol       Date:  2018-05-14       Impact factor: 5.315

10.  Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke.

Authors:  Yuki Shinohara; Noriyuki Takahashi; Yongbum Lee; Tomomi Ohmura; Toshibumi Kinoshita
Journal:  Jpn J Radiol       Date:  2019-10-31       Impact factor: 2.374

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