{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T14:29:48Z","timestamp":1756996188650,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030687625"},{"type":"electronic","value":"9783030687632"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68763-2_34","type":"book-chapter","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T16:28:24Z","timestamp":1613838504000},"page":"444-460","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Attention Based Semi-supervised 2D-Pose Estimation for Surgical Instruments"],"prefix":"10.1007","author":[{"given":"Mert","family":"Kayhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Okan","family":"K\u00f6p\u00fckl\u00fc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mhd Hasan","family":"Sarhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehmet","family":"Yigitsoy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abouzar","family":"Eslami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerhard","family":"Rigoll","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"key":"34_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/978-3-319-46478-7_44","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Bulat","year":"2016","unstructured":"Bulat, A., Tzimiropoulos, G.: Human pose estimation via convolutional part heatmap regression. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 717\u2013732. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_44"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291\u20137299 (2017)","DOI":"10.1109\/CVPR.2017.143"},{"key":"34_CR3","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"issue":"5","key":"34_CR4","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1109\/TMI.2017.2787672","volume":"37","author":"X Du","year":"2018","unstructured":"Du, X., et al.: Articulated multi-instrument 2-D pose estimation using fully convolutional networks. IEEE Trans. Med. Imaging 37(5), 1276\u20131287 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"34_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/978-3-030-01258-8_23","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Dvornik","year":"2018","unstructured":"Dvornik, N., Mairal, J., Schmid, C.: Modeling visual context is key to augmenting object detection datasets. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 375\u2013391. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01258-8_23"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Peraza-Herrera, L.C., et al.: ToolNet: holistically-nested real-time segmentation of robotic surgical tools. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5717\u20135722. IEEE (2017)","DOI":"10.1109\/IROS.2017.8206462"},{"key":"34_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-319-54057-3_8","volume-title":"Computer-Assisted and Robotic Endoscopy","author":"LC Garc\u00eda-Peraza-Herrera","year":"2017","unstructured":"Garc\u00eda-Peraza-Herrera, L.C., et al.: Real-time segmentation of non-rigid surgical tools based on deep learning and tracking. In: Peters, T., et al. (eds.) CARE 2016. LNCS, vol. 10170, pp. 84\u201395. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54057-3_8"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"34_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1007\/10704282_132","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI\u201999","author":"PK Gupta","year":"1999","unstructured":"Gupta, P.K., Jensen, P.S., de Juan, E.: Surgical forces and tactile perception during retinal microsurgery. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 1218\u20131225. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/10704282_132"},{"key":"34_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"34_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1007\/978-3-319-66185-8_75","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017","author":"I Laina","year":"2017","unstructured":"Laina, I., et al.: Concurrent segmentation and localization for tracking of surgical instruments. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 664\u2013672. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_75"},{"issue":"8","key":"34_CR12","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2018","unstructured":"Miyato, T., Maeda, S.I., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979\u20131993 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR13","unstructured":"Oliver, A., Odena, A., Raffel, C.A., Cubuk, E.D., Goodfellow, I.: Realistic evaluation of deep semi-supervised learning algorithms. In: Advances in Neural Information Processing Systems, pp. 3235\u20133246 (2018)"},{"key":"34_CR14","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.media.2016.05.003","volume":"34","author":"N Rieke","year":"2016","unstructured":"Rieke, N., et al.: Real-time localization of articulated surgical instruments in retinal microsurgery. Med. Image Anal. 34, 82\u2013100 (2016)","journal-title":"Med. Image Anal."},{"key":"34_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"6","key":"34_CR16","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s11548-017-1565-x","volume":"12","author":"M Sahu","year":"2017","unstructured":"Sahu, M., Mukhopadhyay, A., Szengel, A., Zachow, S.: Addressing multi-label imbalance problem of surgical tool detection using CNN. Int. J. Comput. Assist. Radiol. Surg. 12(6), 1013\u20131020 (2017)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"7","key":"34_CR17","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.1109\/TMI.2017.2665671","volume":"36","author":"D Sarikaya","year":"2017","unstructured":"Sarikaya, D., Corso, J.J., Guru, K.A.: Detection and localization of robotic tools in robot-assisted surgery videos using deep neural networks for region proposal and detection. IEEE Trans. Med. Imaging 36(7), 1542\u20131549 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"34_CR18","series-title":"Applied Mathematical Sciences","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-69277-7","volume-title":"Variational Methods in Imaging","author":"O Scherzer","year":"2009","unstructured":"Scherzer, O., Grasmair, M., Grossauer, H., Haltmeier, M., Lenzen, F.: Variational Methods in Imaging. AMS, vol. 167. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-69277-7"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Speidel, S., et al.: Automatic classification of minimally invasive instruments based on endoscopic image sequences. In: Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, vol. 7261, p. 72610A. International Society for Optics and Photonics (2009)","DOI":"10.1117\/12.811112"},{"issue":"1","key":"34_CR20","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"34_CR21","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1016\/S0090-4295(01)01423-6","volume":"58","author":"GT Sung","year":"2001","unstructured":"Sung, G.T., Gill, I.S.: Robotic laparoscopic surgery: a comparison of the da Vinci and Zeus systems. Urology 58(6), 893\u2013898 (2001)","journal-title":"Urology"},{"issue":"5","key":"34_CR22","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TPAMI.2012.209","volume":"35","author":"R Sznitman","year":"2012","unstructured":"Sznitman, R., Richa, R., Taylor, R.H., Jedynak, B., Hager, G.D.: Unified detection and tracking of instruments during retinal microsurgery. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1263\u20131273 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR23","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, pp. 1195\u20131204 (2017)"},{"key":"34_CR24","unstructured":"Tschannen, M., Bachem, O., Lucic, M.: Recent advances in autoencoder-based representation learning. arXiv preprint arXiv:1812.05069 (2018)"},{"issue":"1","key":"34_CR25","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1137\/0917016","volume":"17","author":"CR Vogel","year":"1996","unstructured":"Vogel, C.R., Oman, M.E.: Iterative methods for total variation denoising. SIAM J. Sci. Comput. 17(1), 227\u2013238 (1996)","journal-title":"SIAM J. Sci. Comput."},{"key":"34_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01261-8_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Wu","year":"2018","unstructured":"Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_1"},{"key":"34_CR27","unstructured":"Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015)"},{"key":"34_CR28","unstructured":"Yalniz, I.Z., J\u00e9gou, H., Chen, K., Paluri, M., Mahajan, D.: Billion-scale semi-supervised learning for image classification. arXiv preprint arXiv:1905.00546 (2019)"},{"key":"34_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-319-46720-7_45","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"M Ye","year":"2016","unstructured":"Ye, M., Zhang, L., Giannarou, S., Yang, G.-Z.: Real-time 3D tracking of articulated tools for robotic surgery. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 386\u2013394. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46720-7_45"},{"key":"34_CR30","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. arXiv preprint arXiv:1708.04896 (2017)"},{"issue":"3","key":"34_CR31","doi-asserted-by":"publisher","first-page":"234","DOI":"10.12720\/joace.2.3.234-241","volume":"2","author":"J Zhou","year":"2014","unstructured":"Zhou, J., Payandeh, S.: Visual tracking of laparoscopic instruments. J. Autom. Control Eng. 2(3), 234\u2013241 (2014)","journal-title":"J. Autom. Control Eng."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68763-2_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T17:36:02Z","timestamp":1613842562000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68763-2_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687625","9783030687632"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68763-2_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ICPR2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icpr2020.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}