{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:40Z","timestamp":1750220320719,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":12,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"DOI":"10.1145\/3511176.3511189","type":"proceedings-article","created":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T23:18:23Z","timestamp":1647127103000},"page":"76-84","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring bias in F-score computation methods of multi-class segmentation models"],"prefix":"10.1145","author":[{"given":"Lisa","family":"Schneider","sequence":"first","affiliation":[{"name":"Department of Oral Diagnostics,Digital Health and Health Services Research, Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Germany and Topic Group Dental Diagnostics and Digital Dentistry, ITU\/WHO Focus Group on AI for Health, Switzerland"}]},{"given":"Palak","family":"Dave","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Florida, USA"}]},{"given":"Lubaina","family":"Arsiwala-Scheppach","sequence":"additional","affiliation":[{"name":"Department of Oral Diagnostics,Digital Health and Health Services Research, Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Germany and Topic Group Dental Diagnostics and Digital Dentistry, ITU\/WHO Focus Group on AI for Health, Switzerland"}]},{"given":"Falk","family":"Schwendicke","sequence":"additional","affiliation":[{"name":"Department of Oral Diagnostics,Digital Health and Health Services Research, Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Germany and Topic Group Dental Diagnostics and Digital Dentistry, ITU\/WHO Focus Group on AI for Health, Switzerland"}]},{"given":"Joachim","family":"Krois","sequence":"additional","affiliation":[{"name":"Department of Oral Diagnostics,Digital Health and Health Services Research, Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Germany and Topic Group Dental Diagnostics and Digital Dentistry, ITU\/WHO Focus Group on AI for Health, Switzerland"}]}],"member":"320","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Medical image analysis, 63:101692","author":"Akil Mohamed","year":"2020","unstructured":"Mohamed Akil , Rachida Saouli , Rostom Kachouri , Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Medical image analysis, 63:101692 , 2020 . Mohamed Akil, Rachida Saouli, Rostom Kachouri, Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Medical image analysis, 63:101692, 2020."},{"key":"e_1_3_2_1_2_1","volume-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1):289\u2013300","author":"Benjamini Yoav","year":"1995","unstructured":"Yoav Benjamini and Yosef Hochberg . Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1):289\u2013300 , 1995 . Yoav Benjamini and Yosef Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1):289\u2013300, 1995."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2020.103425"},{"key":"e_1_3_2_1_4_1","volume-title":"Residual attention u-net for automated multiclass segmentation of covid-19 chest ct images. arXiv preprint arXiv:2004.05645","author":"Chen Xiaocong","year":"2020","unstructured":"Xiaocong Chen , Lina Yao , and Yu Zhang . Residual attention u-net for automated multiclass segmentation of covid-19 chest ct images. arXiv preprint arXiv:2004.05645 , 2020 . Xiaocong Chen, Lina Yao, and Yu Zhang. Residual attention u-net for automated multiclass segmentation of covid-19 chest ct images. arXiv preprint arXiv:2004.05645, 2020."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1882471.1882479"},{"key":"e_1_3_2_1_6_1","volume-title":"Attention u-net based adversarial architectures for chest x-ray lung segmentation. arXiv preprint arXiv:2003.10304","author":"Ga\u00e1l Guszt\u00e1v","year":"2020","unstructured":"Guszt\u00e1v Ga\u00e1l , Bal\u00e1zs Maga , and Andr\u00e1s Luk\u00e1cs . Attention u-net based adversarial architectures for chest x-ray lung segmentation. arXiv preprint arXiv:2003.10304 , 2020 . Guszt\u00e1v Ga\u00e1l, Bal\u00e1zs Maga, and Andr\u00e1s Luk\u00e1cs. Attention u-net based adversarial architectures for chest x-ray lung segmentation. arXiv preprint arXiv:2003.10304, 2020."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_8_1","volume-title":"International Society for Optics and Photonics","author":"Holste Gregory","year":"2020","unstructured":"Gregory Holste , Ryan Sullivan , Michael Bindschadler , Nicholas Nagy , and Adam Alessio . Multi-class semantic segmentation of pediatric chest radiographs. In Medical Imaging 2020: Image Processing, volume 11313, page 113131E . International Society for Optics and Photonics , 2020 . Gregory Holste, Ryan Sullivan, Michael Bindschadler, Nicholas Nagy, and Adam Alessio. Multi-class semantic segmentation of pediatric chest radiographs. In Medical Imaging 2020: Image Processing, volume 11313, page 113131E. International Society for Optics and Photonics, 2020."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_10_1","volume-title":"Journal of Dental Research, J Dent Res 100 (Spec Iss B):0246","author":"Rohrer Csaba","year":"2021","unstructured":"Csaba Rohrer , Krois Joachim , Jonas Almeida Rodrigues, and Falk Schwendicke. Segmentation of dental restorations on panoramic radiographs using deep learning . In Journal of Dental Research, J Dent Res 100 (Spec Iss B):0246 , 2021 . Csaba Rohrer, Krois Joachim, Jonas Almeida Rodrigues, and Falk Schwendicke. Segmentation of dental restorations on panoramic radiographs using deep learning. In Journal of Dental Research, J Dent Res 100 (Spec Iss B):0246, 2021."},{"key":"e_1_3_2_1_11_1","first-page":"241","volume-title":"International Conference on Medical image computing and computer-assisted intervention","author":"Ronneberger Olaf","unstructured":"Olaf Ronneberger , Philipp Fischer , and Thomas Brox . U-net : Convolutional networks for biomedical image segmentation . In International Conference on Medical image computing and computer-assisted intervention , pages 234\u2013 241 . Springer, 2015. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234\u2013241. Springer, 2015."},{"key":"e_1_3_2_1_12_1","volume-title":"Springer","author":"Wilcoxon Frank","year":"1992","unstructured":"Frank Wilcoxon . Individual comparisons by ranking methods. In Breakthroughs in statistics, pages 196\u2013202 . Springer , 1992 . Frank Wilcoxon. Individual comparisons by ranking methods. In Breakthroughs in statistics, pages 196\u2013202. Springer, 1992."}],"event":{"name":"ICVIP 2021: 2021 The 5th International Conference on Video and Image Processing","acronym":"ICVIP 2021","location":"Hayward CA USA"},"container-title":["2021 The 5th International Conference on Video and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511176.3511189","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511176.3511189","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:58Z","timestamp":1750191118000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511176.3511189"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,22]]},"references-count":12,"alternative-id":["10.1145\/3511176.3511189","10.1145\/3511176"],"URL":"https:\/\/doi.org\/10.1145\/3511176.3511189","relation":{},"subject":[],"published":{"date-parts":[[2021,12,22]]},"assertion":[{"value":"2022-03-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}