Literature DB >> 30706381

Recent technical development of artificial intelligence for diagnostic medical imaging.

Norio Nakata1.   

Abstract

Deep learning has caused a third boom of artificial intelligence and great changes of diagnostic medical imaging systems such as radiology, pathology, retinal imaging, dermatology inspection, and endoscopic diagnosis will be expected in the near future. However, various attempts and new methods of deep learning have been proposed in recent years, and their progress is extremely fast. Therefore, at the initial stage when medical artificial intelligence papers were published, the artificial intelligence technology itself may be old technology or well-known general-purpose common technology. Therefore, the author has reviewed state-of-the-art computer vision papers and presentations of 2018 using deep learning technologies, which will have future clinical potentials selected from the point of view of a radiologist such as generative adversarial network, knowledge distillation, and general image data sets for supervised learning.

Keywords:  Artificial intelligence; Computer vision; Deep learning

Mesh:

Year:  2019        PMID: 30706381     DOI: 10.1007/s11604-018-0804-6

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


  8 in total

Review 1.  Artificial intelligence: a critical review of current applications in pancreatic imaging.

Authors:  Maxime Barat; Guillaume Chassagnon; Anthony Dohan; Sébastien Gaujoux; Romain Coriat; Christine Hoeffel; Christophe Cassinotto; Philippe Soyer
Journal:  Jpn J Radiol       Date:  2021-02-06       Impact factor: 2.374

2.  Celebrating the beginning of international journal collaboration.

Authors:  Shinji Naganawa; Yukunori Korogi
Journal:  Jpn J Radiol       Date:  2020-01       Impact factor: 2.374

3.  How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021.

Authors:  Mohammad H Rezazade Mehrizi; Simon H Gerritsen; Wouter M de Klerk; Chantal Houtschild; Silke M H Dinnessen; Luna Zhao; Rik van Sommeren; Abby Zerfu
Journal:  Eur Radiol       Date:  2022-08-18       Impact factor: 7.034

4.  Three-dimensional conditional generative adversarial network-based virtual thin-slice technique for the morphological evaluation of the spine.

Authors:  Atsushi Nakamoto; Masatoshi Hori; Hiromitsu Onishi; Takashi Ota; Hideyuki Fukui; Kazuya Ogawa; Jun Masumoto; Akira Kudo; Yoshiro Kitamura; Shoji Kido; Noriyuki Tomiyama
Journal:  Sci Rep       Date:  2022-07-16       Impact factor: 4.996

5.  Deep transfer learning to quantify pleural effusion severity in chest X-rays.

Authors:  Tao Huang; Rui Yang; Longbin Shen; Aozi Feng; Li Li; Ningxia He; Shuna Li; Liying Huang; Jun Lyu
Journal:  BMC Med Imaging       Date:  2022-05-27       Impact factor: 2.795

Review 6.  Artificial intelligence and computational pathology.

Authors:  Miao Cui; David Y Zhang
Journal:  Lab Invest       Date:  2021-01-16       Impact factor: 5.662

7.  An update on radiomics techniques in primary liver cancers.

Authors:  Vincenza Granata; Roberta Fusco; Sergio Venazio Setola; Igino Simonetti; Diletta Cozzi; Giulia Grazzini; Francesca Grassi; Andrea Belli; Vittorio Miele; Francesco Izzo; Antonella Petrillo
Journal:  Infect Agent Cancer       Date:  2022-03-04       Impact factor: 2.965

8.  Radiomics in hepatic metastasis by colorectal cancer.

Authors:  Vincenza Granata; Roberta Fusco; Maria Luisa Barretta; Carmine Picone; Antonio Avallone; Andrea Belli; Renato Patrone; Marilina Ferrante; Diletta Cozzi; Roberta Grassi; Roberto Grassi; Francesco Izzo; Antonella Petrillo
Journal:  Infect Agent Cancer       Date:  2021-06-02       Impact factor: 2.965

  8 in total

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