{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:50:39Z","timestamp":1776077439903,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["281474342\/GRK2224\/1"],"award-info":[{"award-number":["281474342\/GRK2224\/1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Accurate and fast assessment of resection margins is an essential part of a dermatopathologist\u2019s clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have a high probability of exhibiting pathological features in whole slide images (WSI). We focus on detecting basal cell carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues\u2019 exact location on 100 WSI. The rest of the data, with ground-truth sectionwise labels, are used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: (a) deep supervision, (b) linear combination of decoder outputs, and obtain some interpretations about what the network\u2019s decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the Test set.<\/jats:p>","DOI":"10.3390\/jimaging7040071","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T10:58:07Z","timestamp":1618311487000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1123-6110","authenticated-orcid":false,"given":"Jean","family":"Le\u2019Clerc Arrastia","sequence":"first","affiliation":[{"name":"Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7693-8618","authenticated-orcid":false,"given":"Nick","family":"Heilenk\u00f6tter","sequence":"additional","affiliation":[{"name":"Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6550-6043","authenticated-orcid":false,"given":"Daniel","family":"Otero Baguer","sequence":"additional","affiliation":[{"name":"Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lena","family":"Hauberg-Lotte","sequence":"additional","affiliation":[{"name":"Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5233-7962","authenticated-orcid":false,"given":"Tobias","family":"Boskamp","sequence":"additional","affiliation":[{"name":"SCiLS, Bruker Daltonik, 28359 Bremen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonja","family":"Hetzer","sequence":"additional","affiliation":[{"name":"Dermatopathologie Duisburg Essen, 45329 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole","family":"Duschner","sequence":"additional","affiliation":[{"name":"Dermatopathologie Duisburg Essen, 45329 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Schaller","sequence":"additional","affiliation":[{"name":"Dermatopathologie Duisburg Essen, 45329 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1448-8345","authenticated-orcid":false,"given":"Peter","family":"Maass","sequence":"additional","affiliation":[{"name":"Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. 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