Domain adaptation and self-supervised learning for surgical margin detection

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 ti...

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Bibliographic Details
Published in:International journal for computer assisted radiology and surgery 2021-05, Vol.16 (5), p.861-869
Main Authors: Santilli, Alice M. L., Jamzad, Amoon, Sedghi, Alireza, Kaufmann, Martin, Logan, Kathryn, Wallis, Julie, Ren, Kevin Y. M, Janssen, Natasja, Merchant, Shaila, Engel, Jay, McKay, Doug, Varma, Sonal, Wang, Ami, Fichtinger, Gabor, Rudan, John F., Mousavi, Parvin
Format: Article
Language:English
Subjects:
Adaptation
Burns
Cancer
Classification
Classifiers
Computer Imaging
Computer Science
Data collection
Datasets
Domains
Health Informatics
Imaging
Mass spectrometry
Medicine
Medicine & Public Health
Metabolites
Original Article
Pattern Recognition and Graphics
Radiology
Real time
Self-supervised learning
Statistical methods
Supervised learning
Surgery
Tumors
Vision
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Summary: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. Methods We address these challenges by first, building a self-supervised learning model from limited, weakly labeled data. By doing so, the model can learn to contextualize the general features of iKnife data from a more accessible cancer type. Second, the trained model can then be applied to a cancer classification task on breast data. This domain adaptation allows for the transfer of learnt weights from models of one tissue type to another. Results Our datasets contained 320 skin burns (129 tumor burns, 191 normal burns) from 51 patients and 144 breast tissue burns (41 tumor and 103 normal) from 11 patients. We investigate the effect of different hyper-parameters on the performance of the final classifier. The proposed two-step method performed statistically significantly better than a baseline model ( p -value 
ISSN:1861-6410
1861-6429
DOI:10.1007/s11548-021-02381-6