English

Assessing unconstrained surgical cuttings in VR using CNNs

Computer Vision and Pattern Recognition 2022-05-03 v1 Artificial Intelligence Graphics
Authors: Ilias Chrysovergis , Manos Kamarianakis , Mike Kentros , Dimitris Angelis , Antonis Protopsaltis , George Papagiannakis
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Abstract

We present a Convolutional Neural Network (CNN) suitable to assess unconstrained surgical cuttings, trained on a dataset created with a data augmentation technique.

Keywords

convolutional neural network medical image analysis surgical robotics

Cite

@article{arxiv.2205.00934,
  title  = {Assessing unconstrained surgical cuttings in VR using CNNs},
  author = {Ilias Chrysovergis and Manos Kamarianakis and Mike Kentros and Dimitris Angelis and Antonis Protopsaltis and George Papagiannakis},
  journal= {arXiv preprint arXiv:2205.00934},
  year   = {2022}
}

Comments

2 pages, 2 figures, Submitted to the Siggraph '22 Poster Session (Vancouver, 8-11 Aug 2022)

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