{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:56:58Z","timestamp":1776923818695,"version":"3.51.2"},"reference-count":89,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Universidade de Lisboa","doi-asserted-by":"publisher","award":["SFRH\/BD\/135733\/2018"],"award-info":[{"award-number":["SFRH\/BD\/135733\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Universidade de Lisboa","doi-asserted-by":"publisher","award":["UIDB\/00645\/2020"],"award-info":[{"award-number":["UIDB\/00645\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2014Portugal","award":["SFRH\/BD\/135733\/2018"],"award-info":[{"award-number":["SFRH\/BD\/135733\/2018"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2014Portugal","award":["UIDB\/00645\/2020"],"award-info":[{"award-number":["UIDB\/00645\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images.<\/jats:p>","DOI":"10.3390\/jimaging8090231","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T21:01:31Z","timestamp":1661806891000},"page":"231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1931-294X","authenticated-orcid":false,"given":"Ana M.","family":"Mota","sequence":"first","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"given":"Matthew J.","family":"Clarkson","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering and the Centre for Medical Image Computing, University College London, London WC1E 6BT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5247-4011","authenticated-orcid":false,"given":"Pedro","family":"Almeida","sequence":"additional","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8048-7896","authenticated-orcid":false,"given":"Nuno","family":"Matela","sequence":"additional","affiliation":[{"name":"Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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