{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T09:50:45Z","timestamp":1792403445011,"version":"4.4.0"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,4]],"date-time":"2020-07-04T00:00:00Z","timestamp":1593820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Radiological Society Bayer Research Grant 2018","award":["\uff382986"],"award-info":[{"award-number":["\uff382986"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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 \u00d7 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 &lt; 0.001), and benign masses had significantly higher scores than normal tissues (p &lt; 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.<\/jats:p>","DOI":"10.3390\/diagnostics10070456","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T03:19:27Z","timestamp":1594005567000},"page":"456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7141-8901","authenticated-orcid":false,"given":"Tomoyuki","family":"Fujioka","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3240-4910","authenticated-orcid":false,"given":"Kazunori","family":"Kubota","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"},{"name":"Department of Radiology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi 321-0293, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6107-4519","authenticated-orcid":false,"given":"Mio","family":"Mori","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuka","family":"Kikuchi","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leona","family":"Katsuta","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mizuki","family":"Kimura","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5931-6507","authenticated-orcid":false,"given":"Emi","family":"Yamaga","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mio","family":"Adachi","sequence":"additional","affiliation":[{"name":"Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Goshi","family":"Oda","sequence":"additional","affiliation":[{"name":"Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1967-0445","authenticated-orcid":false,"given":"Tsuyoshi","family":"Nakagawa","sequence":"additional","affiliation":[{"name":"Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshio","family":"Kitazume","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ukihide","family":"Tateishi","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21442","article-title":"Cancer Statistics","volume":"68","author":"Siegel","year":"2018","journal-title":"CA Cancer J. 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