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      Surgical tool datasets for machine learning research: A survey

      Rodrigues, Mark William; Mayo, Michael; Patros, Panos
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      Rodrigues2022_Article_SurgicalToolDatasetsForMachine.pdf
      Published version, 2.021Mb
      DOI
       10.1007/s11263-022-01640-6
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      Rodrigues, M., Mayo, M. & Patros, P. Surgical Tool Datasets for Machine Learning Research: A Survey. Int J Comput Vis (2022). https://doi.org/10.1007/s11263-022-01640-6
      Permanent Research Commons link: https://hdl.handle.net/10289/15000
      Abstract
      This paper is a comprehensive survey of datasets for surgical tool detection and related surgical data science and machine learning techniques and algorithms. The survey offers a high level perspective of current research in this area, analyses the taxonomy of approaches adopted by researchers using surgical tool datasets, and addresses key areas of research, such as the datasets used, evaluation metrics applied and deep learning techniques utilised. Our presentation and taxonomy provides a framework that facilitates greater understanding of current work, and highlights the challenges and opportunities for further innovative and useful research.
      Date
      2022
      Type
      Journal Article
      Publisher
      Springer
      Rights
      Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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      • Computing and Mathematical Sciences Papers [1458]
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