Dr. Shaila Merchant Profile

Dr. Shaila Merchant

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Proceedings Article | 3 April 2023 Presentation + Paper

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

Proceedings Volume 12466, 124660B (2023) https://doi.org/10.1117/12.2654104

KEYWORDS: Data modeling, Tumor growth modeling, Education and training, Tissues, Cancer, Surgery, Machine learning, Breast cancer, Tumors, Performance modeling

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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.

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