Self-supervised learning and uncertainty estimation for surgical margin detection

Ayesha Syeda; Fahimeh Fooladgar; Amoon Jamzad; Dilakshan Srikanthan; Martin Kaufmann; Kevin Ren; Jay Engel; Ross Walker; Shaila Merchant; Doug McKay; Sonal Varma; Gabor Fichtinger; John Rudan; Parvin Mousavi

doi:10.1117/12.2654104

3 April 2023 Self-supervised learning and uncertainty estimation for surgical margin detection

Ayesha Syeda, Fahimeh Fooladgar, Amoon Jamzad, Dilakshan Srikanthan, Martin Kaufmann, Kevin Ren, Jay Engel, Ross Walker, Shaila Merchant, Doug McKay, Sonal Varma, Gabor Fichtinger, John Rudan, Parvin Mousavi

Author Affiliations +

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 124660B (2023) https://doi.org/10.1117/12.2654104
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Up to 35% of breast-conserving surgeries fail to resect all the tumors completely. Ideally, machine learning methods using the iKnife data, which uses Rapid Evaporative Ionization Mass Spectrometry (REIMS), can be utilized to predict tissue type in real-time during surgery, resulting in better tumor resections. As REIMS data is heterogeneous and weakly labeled, and datasets are often small, model performance and reliability can be adversely affected. Self-supervised training and uncertainty estimation of the prediction can be used to mitigate these challenges by learning the signatures of input data without their label as well as including predictive confidence in output reporting. We first design an autoencoder model using a reconstruction pretext task as a self-supervised pretraining step without considering tissue type. Next, we construct our uncertainty-aware classifier using the encoder part of the model with Masksembles layers to estimate the uncertainty associated with its predictions. The pretext task was trained on 190 burns collected from 34 patients from Basal Cell Carcinoma iKnife data. The model was further trained on breast cancer data comprising of 200 burns collected from 15 patients. Our proposed model shows improvement in sensitivity and uncertainty metrics of 10% and 15.7% over the baseline, respectively. The proposed strategies lead to improvements in uncertainty calibration and overall performance, toward reducing the likelihood of incomplete resection, supporting removal of minimal non-neoplastic tissue, and improved model reliability during surgery. Future work will focus on further testing the model on intraoperative data and additional exvivo data following collection of more breast samples.

Conference Presentation

Citation Download Citation

Ayesha Syeda, Fahimeh Fooladgar, Amoon Jamzad, Dilakshan Srikanthan, Martin Kaufmann, Kevin Ren, Jay Engel, Ross Walker, Shaila Merchant, Doug McKay, Sonal Varma, Gabor Fichtinger, John Rudan, and Parvin Mousavi "Self-supervised learning and uncertainty estimation for surgical margin detection", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124660B (3 April 2023); https://doi.org/10.1117/12.2654104

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available