{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:20:36Z","timestamp":1784737236489,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"MIAI@Grenoble Alpes","award":["ANR-19-P3IA-0003"],"award-info":[{"award-number":["ANR-19-P3IA-0003"]}]},{"name":"SATT Linksium"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,14]]},"DOI":"10.1145\/3549555.3549570","type":"proceedings-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T16:14:01Z","timestamp":1665159241000},"page":"111-115","source":"Crossref","is-referenced-by-count":2,"title":["Segmenting partially annotated medical images"],"prefix":"10.1145","author":[{"given":"Nicolas","family":"Martin","sequence":"first","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Pierre","family":"Chevallet","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georges","family":"Qu\u00e9not","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"2640","volume-title":"Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning. PMLR, 84\u201391","author":"Bokhorst John-Melle","year":"2019","unstructured":"John-Melle Bokhorst , Hans Pinckaers , Peter\u00a0van Zwam , Iris Nagtegaal , Jeroen van\u00a0der Laak , and Francesco Ciompi . 2019 . Learning from sparsely annotated data for semantic segmentation in histopathology images . In Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning. PMLR, 84\u201391 . https:\/\/proceedings.mlr.press\/v102\/bokhorst19a.html ISSN: 2640 - 3498 . John-Melle Bokhorst, Hans Pinckaers, Peter\u00a0van Zwam, Iris Nagtegaal, Jeroen van\u00a0der Laak, and Francesco Ciompi. 2019. Learning from sparsely annotated data for semantic segmentation in histopathology images. In Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning. PMLR, 84\u201391. https:\/\/proceedings.mlr.press\/v102\/bokhorst19a.html ISSN: 2640-3498."},{"key":"e_1_3_2_1_2_1","unstructured":"Yigit\u00a0B. Can Krishna Chaitanya Basil Mustafa Lisa\u00a0M. Koch Ender Konukoglu and Christian\u00a0F. Baumgartner. 2018. Learning to Segment Medical Images with Scribble-Supervision Alone. (2018). https:\/\/doi.org\/10.48550\/ARXIV.1807.04668 Publisher: arXiv Version Number: 1.  Yigit\u00a0B. Can Krishna Chaitanya Basil Mustafa Lisa\u00a0M. Koch Ender Konukoglu and Christian\u00a0F. Baumgartner. 2018. Learning to Segment Medical Images with Scribble-Supervision Alone. (2018). https:\/\/doi.org\/10.48550\/ARXIV.1807.04668 Publisher: arXiv Version Number: 1."},{"key":"e_1_3_2_1_3_1","volume-title":"BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. arXiv:1503.01640 [cs] (May","author":"Dai Jifeng","year":"2015","unstructured":"Jifeng Dai , Kaiming He , and Jian Sun . 2015. BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. arXiv:1503.01640 [cs] (May 2015 ). http:\/\/arxiv.org\/abs\/1503.01640 arXiv:1503.01640. Jifeng Dai, Kaiming He, and Jian Sun. 2015. BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. arXiv:1503.01640 [cs] (May 2015). http:\/\/arxiv.org\/abs\/1503.01640 arXiv:1503.01640."},{"key":"e_1_3_2_1_4_1","volume-title":"Conference on Colour in Graphics.","author":"Eckhard Timo","year":"2012","unstructured":"Timo Eckhard , E. Valero , and Juan Nieves . 2012 . Labial teeth and gingiva color image segmentation for gingival health-state assessment . In Conference on Colour in Graphics. Timo Eckhard, E. Valero, and Juan Nieves. 2012. Labial teeth and gingiva color image segmentation for gingival health-state assessment. In Conference on Colour in Graphics."},{"key":"e_1_3_2_1_5_1","volume-title":"2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). 1265\u20131268","author":"Gao Mingchen","year":"2016","unstructured":"Mingchen Gao , Ziyue Xu , Le Lu , Aaron Wu , Isabella Nogues , Ronald\u00a0 M. Summers , and Daniel\u00a0 J. Mollura . 2016 . Segmentation label propagation using deep convolutional neural networks and dense conditional random field . In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). 1265\u20131268 . https:\/\/doi.org\/10.1109\/ISBI.2016.7493497 ISSN: 1945-8452. Mingchen Gao, Ziyue Xu, Le Lu, Aaron Wu, Isabella Nogues, Ronald\u00a0M. Summers, and Daniel\u00a0J. Mollura. 2016. Segmentation label propagation using deep convolutional neural networks and dense conditional random field. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). 1265\u20131268. https:\/\/doi.org\/10.1109\/ISBI.2016.7493497 ISSN: 1945-8452."},{"key":"e_1_3_2_1_6_1","volume-title":"arXiv:1703.06870 [cs] (Jan","author":"He Kaiming","year":"2018","unstructured":"Kaiming He , Georgia Gkioxari , Piotr Doll\u00e1r , and Ross Girshick . 2018. Mask R-CNN. arXiv:1703.06870 [cs] (Jan . 2018 ). http:\/\/arxiv.org\/abs\/1703.06870 arXiv:1703.06870. Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross Girshick. 2018. Mask R-CNN. arXiv:1703.06870 [cs] (Jan. 2018). http:\/\/arxiv.org\/abs\/1703.06870 arXiv:1703.06870."},{"key":"e_1_3_2_1_7_1","volume-title":"Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs] (Dec","author":"He Kaiming","year":"2015","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2015. Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs] (Dec . 2015 ). arXiv:1512.03385. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs] (Dec. 2015). arXiv:1512.03385."},{"key":"e_1_3_2_1_8_1","first-page":"4","article-title":"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges","volume":"32","author":"Hesamian Mohammad\u00a0Hesam","year":"2019","unstructured":"Mohammad\u00a0Hesam Hesamian , Wenjing Jia , Xiangjian He , and Paul Kennedy . 2019 . Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges . Journal of Digital Imaging 32 , 4 (Aug. 2019), 582\u2013596. https:\/\/doi.org\/10.1007\/s10278-019-00227-x Mohammad\u00a0Hesam Hesamian, Wenjing Jia, Xiangjian He, and Paul Kennedy. 2019. Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. Journal of Digital Imaging 32, 4 (Aug. 2019), 582\u2013596. https:\/\/doi.org\/10.1007\/s10278-019-00227-x","journal-title":"Journal of Digital Imaging"},{"key":"e_1_3_2_1_9_1","first-page":"20","article-title":"Oral and Dental Spectral Image Database\u2014ODSI-DB","volume":"10","author":"Hyttinen Joni","year":"2020","unstructured":"Joni Hyttinen , Pauli F\u00e4lt , Heli J\u00e4sberg , Arja Kullaa , and Markku Hauta-Kasari . 2020 . Oral and Dental Spectral Image Database\u2014ODSI-DB . Applied Sciences 10 , 20 (Oct. 2020), 7246. https:\/\/doi.org\/10.3390\/app10207246 Joni Hyttinen, Pauli F\u00e4lt, Heli J\u00e4sberg, Arja Kullaa, and Markku Hauta-Kasari. 2020. Oral and Dental Spectral Image Database\u2014ODSI-DB. Applied Sciences 10, 20 (Oct. 2020), 7246. https:\/\/doi.org\/10.3390\/app10207246","journal-title":"Applied Sciences"},{"key":"e_1_3_2_1_10_1","volume-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (Feb.","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (Feb. 2015). arXiv:1502.03167. Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs] (Feb. 2015). arXiv:1502.03167."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"e_1_3_2_1_12_1","volume-title":"Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W.\u00a0M. van\u00a0der Laak, Bram van Ginneken, and Clara\u00a0I. S\u00e1nchez.","author":"Litjens Geert","year":"2017","unstructured":"Geert Litjens , Thijs Kooi , Babak\u00a0Ehteshami Bejnordi , Arnaud Arindra\u00a0Adiyoso Setio , Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W.\u00a0M. van\u00a0der Laak, Bram van Ginneken, and Clara\u00a0I. S\u00e1nchez. 2017 . A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis 42 (Dec. 2017), 60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005 arXiv:1702.05747. Geert Litjens, Thijs Kooi, Babak\u00a0Ehteshami Bejnordi, Arnaud Arindra\u00a0Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W.\u00a0M. van\u00a0der Laak, Bram van Ginneken, and Clara\u00a0I. S\u00e1nchez. 2017. A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis 42 (Dec. 2017), 60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005 arXiv:1702.05747."},{"key":"e_1_3_2_1_13_1","volume-title":"An integrated iterative annotation technique for easing neural network training in medical image analysis. Nature Machine Intelligence 1, 2 (Feb","author":"Lutnick Brendon","year":"2019","unstructured":"Brendon Lutnick , Brandon Ginley , Darshana Govind , Sean\u00a0 D. McGarry , Peter\u00a0 S. LaViolette , Rabi Yacoub , Sanjay Jain , John\u00a0 E. Tomaszewski , Kuang-Yu Jen , and Pinaki Sarder . 2019. An integrated iterative annotation technique for easing neural network training in medical image analysis. Nature Machine Intelligence 1, 2 (Feb . 2019 ), 112\u2013119. https:\/\/doi.org\/10.1038\/s42256-019-0018-3 Number : 2 Publisher : Nature Publishing Group . Brendon Lutnick, Brandon Ginley, Darshana Govind, Sean\u00a0D. McGarry, Peter\u00a0S. LaViolette, Rabi Yacoub, Sanjay Jain, John\u00a0E. Tomaszewski, Kuang-Yu Jen, and Pinaki Sarder. 2019. An integrated iterative annotation technique for easing neural network training in medical image analysis. Nature Machine Intelligence 1, 2 (Feb. 2019), 112\u2013119. https:\/\/doi.org\/10.1038\/s42256-019-0018-3 Number: 2 Publisher: Nature Publishing Group."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"e_1_3_2_1_15_1","volume-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv:1506.01497 [cs] (Jan","author":"Ren Shaoqing","year":"2016","unstructured":"Shaoqing Ren , Kaiming He , Ross Girshick , and Jian Sun . 2016. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv:1506.01497 [cs] (Jan . 2016 ). http:\/\/arxiv.org\/abs\/1506.01497 arXiv:1506.01497. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2016. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv:1506.01497 [cs] (Jan. 2016). http:\/\/arxiv.org\/abs\/1506.01497 arXiv:1506.01497."},{"key":"e_1_3_2_1_16_1","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597 [cs] (May","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger , Philipp Fischer , and Thomas Brox . 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597 [cs] (May 2015 ). http:\/\/arxiv.org\/abs\/1505.04597 arXiv:1505.04597. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597 [cs] (May 2015). http:\/\/arxiv.org\/abs\/1505.04597 arXiv:1505.04597."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_18_1","volume-title":"Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis 63 (July","author":"Tajbakhsh Nima","year":"2020","unstructured":"Nima Tajbakhsh , Laura Jeyaseelan , Qian Li , Jeffrey\u00a0 N. Chiang , Zhihao Wu , and Xiaowei Ding . 2020. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis 63 (July 2020 ), 101693. https:\/\/doi.org\/10.1016\/j.media.2020.101693 Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey\u00a0N. Chiang, Zhihao Wu, and Xiaowei Ding. 2020. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis 63 (July 2020), 101693. https:\/\/doi.org\/10.1016\/j.media.2020.101693"},{"key":"e_1_3_2_1_19_1","unstructured":"Maxim Tkachenko Mikhail Malyuk Nikita Shevchenko Andrey Holmanyuk and Nikolai Liubimov. 2020-2021. Label Studio: Data labeling software. Open source software available from https:\/\/github.com\/heartexlabs\/label-studio.  Maxim Tkachenko Mikhail Malyuk Nikita Shevchenko Andrey Holmanyuk and Nikolai Liubimov. 2020-2021. Label Studio: Data labeling software. Open source software available from https:\/\/github.com\/heartexlabs\/label-studio."},{"key":"e_1_3_2_1_20_1","volume-title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv:1606.06650 [cs] (June","author":"\u00c7i\u00e7ek \u00d6zg\u00fcn","year":"2016","unstructured":"\u00d6zg\u00fcn \u00c7i\u00e7ek , Ahmed Abdulkadir , Soeren\u00a0 S. Lienkamp , Thomas Brox , and Olaf Ronneberger . 2016. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv:1606.06650 [cs] (June 2016 ). http:\/\/arxiv.org\/abs\/1606.06650 arXiv:1606.06650. \u00d6zg\u00fcn \u00c7i\u00e7ek, Ahmed Abdulkadir, Soeren\u00a0S. Lienkamp, Thomas Brox, and Olaf Ronneberger. 2016. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv:1606.06650 [cs] (June 2016). http:\/\/arxiv.org\/abs\/1606.06650 arXiv:1606.06650."}],"event":{"name":"CBMI 2022: International Conference on Content-based Multimedia Indexing","location":"Graz Austria","acronym":"CBMI 2022"},"container-title":["International Conference on Content-based Multimedia Indexing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549555.3549570","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3549555.3549570","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:11Z","timestamp":1750186811000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3549555.3549570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":20,"alternative-id":["10.1145\/3549555.3549570","10.1145\/3549555"],"URL":"https:\/\/doi.org\/10.1145\/3549555.3549570","relation":{},"subject":[],"published":{"date-parts":[[2022,9,14]]}}}