Domain adaptation and self-supervised learning for surgical margin detection.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 1, 2021

Abstract

PURPOSE: One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in surgical smoke. Using this modality and real-time tissue classification, surgeons could remove all cancerous tissue during the initial surgery, improving many facets of patient outcomes. An obstacle in developing a iKnife breast cancer recognition model is the destructive, time-consuming and sensitive nature of the data collection that limits the size of the datasets.

Authors

  • Alice M L Santilli
    School of Computing, Queen's University, Kingston, ON, Canada. 14amls@queensu.ca.
  • Amoon Jamzad
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Alireza Sedghi
  • Martin Kaufmann
    Department of Medicine, Queen's University, Kingston, ON, Canada.
  • Kathryn Logan
    Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
  • Julie Wallis
    Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
  • Kevin Y M Ren
    Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada.
  • Natasja Janssen
    School of Computing, Queen's University, Ontario, Canada.
  • Shaila Merchant
    Department of Surgery, Queen's University, Ontario, Canada.
  • Jay Engel
    Department of Surgery, Queen's University, Ontario, Canada.
  • Doug McKay
    Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Sonal Varma
    Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
  • Ami Wang
    Department of Pathology, Queen's University, Kingston, ON, Canada.
  • Gabor Fichtinger
    Department of Mechanical and Material Engineering, Queen's University, Kingston, ON, Canada.
  • John F Rudan
    Department of Surgery, Queen's University, Kingston, ON, Canada.
  • Parvin Mousavi
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.

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