{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T12:26:16Z","timestamp":1791289576555,"version":"4.3.0"},"reference-count":105,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:00:00Z","timestamp":1653004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods\u2019 performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.<\/jats:p>","DOI":"10.3390\/jimaging8050141","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T13:56:12Z","timestamp":1653054972000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["Image Augmentation Techniques for Mammogram Analysis"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9637-6157","authenticated-orcid":false,"given":"Parita","family":"Oza","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paawan","family":"Sharma","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samir","family":"Patel","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3586-2570","authenticated-orcid":false,"given":"Festus","family":"Adedoyin","sequence":"additional","affiliation":[{"name":"Department of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0707-6131","authenticated-orcid":false,"given":"Alessandro","family":"Bruno","sequence":"additional","affiliation":[{"name":"Department of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. 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