Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy - Takeaways - MDSpire

Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy

  • By

  • Anita Rau

  • P. J. Eddie Edwards

  • Omer F. Ahmad

  • Paul Riordan

  • Mirek Janatka

  • Laurence B. Lovat

  • Danail Stoyanov

  • April 15, 2019

  • 0 min

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  • 1

    Colorectal adenomas affect nearly 20% of the population, with benign polyps potentially progressing to cancer over several years.

  • 2

    Colonoscopy effectiveness is hindered by operator proficiency, with up to 90% miss rates for precancerous lesions among non-expert endoscopists.

  • 3

    Automatic polyp detection algorithms are being developed to enhance detection rates and improve colonoscopy quality through real-time guidance.

  • 4

    3D reconstruction in endoscopy faces challenges due to view-dependent tissue and the limitations of standard colonoscopy equipment.

  • 5

    Conditional generative adversarial networks (cGANs) are being utilized to improve depth prediction and enhance polyp detection in colonoscopy.

Original Source(s)

Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy

  • by Anita Rau, P. J. Eddie Edwards, Omer F. Ahmad, Paul Riordan, Mirek Janatka, Laurence B. Lovat, Danail Stoyanov

  • April 15, 2019

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