Literature DB >> 33291266

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.

Tomoyuki Fujioka1, Mio Mori1, Kazunori Kubota1,2, Jun Oyama1, Emi Yamaga1, Yuka Yashima1, Leona Katsuta1, Kyoko Nomura1, Miyako Nara1,3, Goshi Oda4, Tsuyoshi Nakagawa4, Yoshio Kitazume1, Ukihide Tateishi1.   

Abstract

Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women's health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.

Entities:  

Keywords:  artificial intelligence; breast; deep learning; machine learning; neural network; ultrasound

Year:  2020        PMID: 33291266      PMCID: PMC7762151          DOI: 10.3390/diagnostics10121055

Source DB:  PubMed          Journal:  Diagnostics (Basel)        ISSN: 2075-4418


  45 in total

Review 1.  Current status of breast ultrasound.

Authors:  Anat Kornecki
Journal:  Can Assoc Radiol J       Date:  2010-09-26       Impact factor: 2.248

Review 2.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 3.  A survey on deep learning in medical image analysis.

Authors:  Geert Litjens; Thijs Kooi; Babak Ehteshami Bejnordi; Arnaud Arindra Adiyoso Setio; Francesco Ciompi; Mohsen Ghafoorian; Jeroen A W M van der Laak; Bram van Ginneken; Clara I Sánchez
Journal:  Med Image Anal       Date:  2017-07-26       Impact factor: 8.545

4.  The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database.

Authors:  Stan Benjamens; Pranavsingh Dhunnoo; Bertalan Meskó
Journal:  NPJ Digit Med       Date:  2020-09-11

Review 5.  Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.

Authors:  Abraham Pouliakis; Efrossyni Karakitsou; Niki Margari; Panagiotis Bountris; Maria Haritou; John Panayiotides; Dimitrios Koutsouris; Petros Karakitsos
Journal:  Biomed Eng Comput Biol       Date:  2016-02-18

Review 6.  Artificial intelligence in breast ultrasound.

Authors:  Ge-Ge Wu; Li-Qiang Zhou; Jian-Wei Xu; Jia-Yu Wang; Qi Wei; You-Bin Deng; Xin-Wu Cui; Christoph F Dietrich
Journal:  World J Radiol       Date:  2019-02-28

7.  An RDAU-NET model for lesion segmentation in breast ultrasound images.

Authors:  Zhemin Zhuang; Nan Li; Alex Noel Joseph Raj; Vijayalakshmi G V Mahesh; Shunmin Qiu
Journal:  PLoS One       Date:  2019-08-23       Impact factor: 3.240

8.  Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks.

Authors:  Tomoyuki Fujioka; Mio Mori; Kazunori Kubota; Yuka Kikuchi; Leona Katsuta; Mio Adachi; Goshi Oda; Tsuyoshi Nakagawa; Yoshio Kitazume; Ukihide Tateishi
Journal:  Diagnostics (Basel)       Date:  2019-11-06

9.  Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial.

Authors:  Noriaki Ohuchi; Akihiko Suzuki; Tomotaka Sobue; Masaaki Kawai; Seiichiro Yamamoto; Ying-Fang Zheng; Yoko Narikawa Shiono; Hiroshi Saito; Shinichi Kuriyama; Eriko Tohno; Tokiko Endo; Akira Fukao; Ichiro Tsuji; Takuhiro Yamaguchi; Yasuo Ohashi; Mamoru Fukuda; Takanori Ishida
Journal:  Lancet       Date:  2015-11-05       Impact factor: 79.321

10.  Automated and real-time segmentation of suspicious breast masses using convolutional neural network.

Authors:  Viksit Kumar; Jeremy M Webb; Adriana Gregory; Max Denis; Duane D Meixner; Mahdi Bayat; Dana H Whaley; Mostafa Fatemi; Azra Alizad
Journal:  PLoS One       Date:  2018-05-16       Impact factor: 3.240

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  14 in total

1.  Faster super-resolution ultrasound imaging with a deep learning model for tissue decluttering and contrast agent localization.

Authors:  Katherine G Brown; Scott Chase Waggener; Arthur David Redfern; Kenneth Hoyt
Journal:  Biomed Phys Eng Express       Date:  2021-10-25

2.  CTG-Net: Cross-task guided network for breast ultrasound diagnosis.

Authors:  Kaiwen Yang; Aiga Suzuki; Jiaxing Ye; Hirokazu Nosato; Ayumi Izumori; Hidenori Sakanashi
Journal:  PLoS One       Date:  2022-08-11       Impact factor: 3.752

3.  Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method.

Authors:  Muhammad Junaid Umer; Muhammad Sharif; Seifedine Kadry; Abdullah Alharbi
Journal:  J Pers Med       Date:  2022-04-26

Review 4.  Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma.

Authors:  Revati Sharma; George Kannourakis; Prashanth Prithviraj; Nuzhat Ahmed
Journal:  Front Med (Lausanne)       Date:  2022-06-14

Review 5.  Updates in Artificial Intelligence for Breast Imaging.

Authors:  Manisha Bahl
Journal:  Semin Roentgenol       Date:  2021-12-31       Impact factor: 0.709

6.  Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams.

Authors:  Yiqiu Shen; Farah E Shamout; Jamie R Oliver; Jan Witowski; Kawshik Kannan; Jungkyu Park; Nan Wu; Connor Huddleston; Stacey Wolfson; Alexandra Millet; Robin Ehrenpreis; Divya Awal; Cathy Tyma; Naziya Samreen; Yiming Gao; Chloe Chhor; Stacey Gandhi; Cindy Lee; Sheila Kumari-Subaiya; Cindy Leonard; Reyhan Mohammed; Christopher Moczulski; Jaime Altabet; James Babb; Alana Lewin; Beatriu Reig; Linda Moy; Laura Heacock; Krzysztof J Geras
Journal:  Nat Commun       Date:  2021-09-24       Impact factor: 17.694

7.  Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine.

Authors:  Rasheed Omobolaji Alabi; Alhadi Almangush; Mohammed Elmusrati; Antti A Mäkitie
Journal:  Front Oral Health       Date:  2022-01-11

8.  Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer.

Authors:  Kanae Takahashi; Tomoyuki Fujioka; Jun Oyama; Mio Mori; Emi Yamaga; Yuka Yashima; Tomoki Imokawa; Atsushi Hayashi; Yu Kujiraoka; Junichi Tsuchiya; Goshi Oda; Tsuyoshi Nakagawa; Ukihide Tateishi
Journal:  Tomography       Date:  2022-01-05

9.  Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.

Authors:  Kiran Jabeen; Muhammad Attique Khan; Majed Alhaisoni; Usman Tariq; Yu-Dong Zhang; Ameer Hamza; Artūras Mickus; Robertas Damaševičius
Journal:  Sensors (Basel)       Date:  2022-01-21       Impact factor: 3.576

10.  Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.

Authors:  Rasheed Omobolaji Alabi; Ibrahim O Bello; Omar Youssef; Mohammed Elmusrati; Antti A Mäkitie; Alhadi Almangush
Journal:  Front Oral Health       Date:  2021-07-26
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