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EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

A. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy. (2016)cite arxiv:1602.03012Comment: Video: https://www.youtube.com/watch?v=6v0NWrFOUUM.

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Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016.A. Twinanda, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy. CoRR, (2016)Deep Neural Networks Predict Remaining Surgery Duration from Cholecystectomy Videos.I. Aksamentov, A. Twinanda, D. Mutter, J. Marescaux, and N. Padoy. MICCAI (2), volume 10434 of Lecture Notes in Computer Science, page 586-593. Springer, (2017)In Vivo Evaluation of a Guidance System for Computer Assisted Robotized Needle Insertion Devoted to Small Animals.S. Nicolau, L. Mendoza-Burgos, L. Soler, D. Mutter, and J. Marescaux. MIAR, volume 5128 of Lecture Notes in Computer Science, page 241-250. Springer, (2008)Future-State Predicting LSTM for Early Surgery Type Recognition.S. Kannan, G. Yengera, D. Mutter, J. Marescaux, and N. Padoy. IEEE Trans. Med. Imaging, 39 (3): 556-566 (2020)Toward realistic radiofrequency ablation of hepatic tumors 3D simulation and planning.C. Villard, L. Soler, A. Gangi, D. Mutter, and J. Marescaux. Medical Imaging: Image-Guided Procedures, volume 5367 of SPIE Proceedings, SPIE, (2004)Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass ProceduresS. Ramesh, D. Dall'Alba, C. Gonzalez, T. Yu, P. Mascagni, D. Mutter, J. Marescaux, P. Fiorini, and N. Padoy. (2021)cite arxiv:2102.12218Comment: Accepted to IPCAI 2021.Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos.C. Nwoye, D. Mutter, J. Marescaux, and N. Padoy. CoRR, (2018)Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016.A. Twinanda, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy. CoRR, (2016)Virtual Radiofrequency Ablation of Liver Tumors.C. Villard, L. Soler, N. Papier, V. Agnus, S. Thery, A. Gangi, D. Mutter, and J. Marescaux. IS4TH, volume 2673 of Lecture Notes in Computer Science, page 366-374. Springer, (2003)EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos.A. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy. IEEE Trans. Med. Imaging, 36 (1): 86-97 (2017)

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EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

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EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos