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This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS).
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Mascagni P, Fiorillo C, Urade T, et al (2020) Formalizing video documentation of the critical view of safety in laparoscopic cholecystectomy: a step towards artificial intelligence assistance to improve surgical safety. Surgical endoscopy 34(6):2709–2714
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Alapatt D, Mascagni P, Vardazaryan A, et al (2021) Temporally constrained neural networks (tcnn): A framework for semi-supervised video semantic segmentation. arXiv preprint arXiv:211213815
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Nwoye CI, Padoy N (2022) Data splits and metrics for method benchmarking on surgical action triplet datasets. arXiv preprint arXiv:220405235
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Alapatt D, Murali A, Srivastav V, et al (2023) Jumpstarting surgical computer vision. arXiv preprint arXiv:231205968
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Ban Y, Eckhoff JA, Ward TM, et al (2023) Concept graph neural networks for surgical video understanding. IEEE Transactions on Medical Imaging
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Murali A, Alapatt D, Mascagni P, et al (2023b) Latent graph representations for critical view of safety assessment. IEEE Transactions on Medical Imaging pp 1–1. https://doi.org/10.1109/TMI.2023.3333034
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Mascagni P, Alapatt D, Garcia A, et al (2021a) Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety. arXiv preprint arXiv:210610916
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Mascagni P, Vardazaryan A, Alapatt D, et al (2021b) Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Annals of Surgery
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Murali A, Alapatt D, Mascagni P, et al (2023a) Encoding surgical videos as latent spatiotemporal graphs for object and anatomy-driven reasoning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 647–657
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