Next Article in Journal
Missing Skeletal Muscle Metastases of Papillary Thyroid Carcinoma
Next Article in Special Issue
Multidetector Computed Tomography with Dedicated Protocol for Breast Cancer Locoregional Staging: Feasibility Study
Previous Article in Journal
Integrated Genomic Analysis of Chromosomal Alterations and Mutations in B-Cell Acute Lymphoblastic Leukemia Reveals Distinct Genetic Profiles at Relapse
Previous Article in Special Issue
Mammographic Breast Density and Urbanization: Interactions with BMI, Environmental, Lifestyle, and Other Patient Factors
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Diagnostics
Volume 10
Issue 7
10.3390/diagnostics10070456
diagnostics-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging

by
Tomoyuki Fujioka
1,*,
Kazunori Kubota
1,2,
Mio Mori
1,
Yuka Kikuchi
1,
Leona Katsuta
1,
Mizuki Kimura
1,
Emi Yamaga
1,
Mio Adachi
3,
Goshi Oda
3,
Tsuyoshi Nakagawa
3,
Yoshio Kitazume
1 and
Ukihide Tateishi
1
1
Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
2
Department of Radiology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi 321-0293, Japan
3
Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(7), 456; https://doi.org/10.3390/diagnostics10070456
Received: 14 June 2020 / Revised: 1 July 2020 / Accepted: 2 July 2020 / Published: 4 July 2020
(This article belongs to the Special Issue Multimodality Breast Imaging)
View Full-Text Download PDF
Browse Figures

Abstract

We aimed to use generative adversarial network (GAN)-based anomaly detection to diagnose images of normal tissue, benign masses, or malignant masses on breast ultrasound. We retrospectively collected 531 normal breast ultrasound images from 69 patients. Data augmentation was performed and 6372 (531 × 12) images were available for training. Efficient GAN-based anomaly detection was used to construct a computational model to detect anomalous lesions in images and calculate abnormalities as an anomaly score. Images of 51 normal tissues, 48 benign masses, and 72 malignant masses were analyzed for the test data. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of this anomaly detection model were calculated. Malignant masses had significantly higher anomaly scores than benign masses (p < 0.001), and benign masses had significantly higher scores than normal tissues (p < 0.001). Our anomaly detection model had high sensitivities, specificities, and AUC values for distinguishing normal tissues from benign and malignant masses, with even greater values for distinguishing normal tissues from malignant masses. GAN-based anomaly detection shows high performance for the detection and diagnosis of anomalous lesions in breast ultrasound images. View Full-Text
Keywords: breast imaging; ultrasound; deep learning; anomaly detection; generative adversarial network breast imaging; ultrasound; deep learning; anomaly detection; generative adversarial network
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

MDPI and ACS Style

Fujioka, T.; Kubota, K.; Mori, M.; Kikuchi, Y.; Katsuta, L.; Kimura, M.; Yamaga, E.; Adachi, M.; Oda, G.; Nakagawa, T.; Kitazume, Y.; Tateishi, U. Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging. Diagnostics 2020, 10, 456. https://doi.org/10.3390/diagnostics10070456

AMA Style

Fujioka T, Kubota K, Mori M, Kikuchi Y, Katsuta L, Kimura M, Yamaga E, Adachi M, Oda G, Nakagawa T, Kitazume Y, Tateishi U. Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging. Diagnostics. 2020; 10(7):456. https://doi.org/10.3390/diagnostics10070456

Chicago/Turabian Style

Fujioka, Tomoyuki, Kazunori Kubota, Mio Mori, Yuka Kikuchi, Leona Katsuta, Mizuki Kimura, Emi Yamaga, Mio Adachi, Goshi Oda, Tsuyoshi Nakagawa, Yoshio Kitazume, and Ukihide Tateishi. 2020. "Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging" Diagnostics 10, no. 7: 456. https://doi.org/10.3390/diagnostics10070456

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop