Self-supervised learning and uncertainty estimation for surgical margin detection with mass spectrometry

Abstract

Breast cancer represents 25% of all new cancer cases and is the second leading cause of death from cancer in Canadian women. The preferred treatment for breast cancer patients is breast conserving surgery, which aims to to minimize the benign tissue removed, while removing all the tumor. The iKnife, which uses rapid evaporative ionization mass spectrometry (REIMS) to provide real-time feedback on tissue type during surgery, has shown promise in reducing the likelihood of incomplete resection. However, the heterogeneity of cancer tissue, small dataset size and coarse labels for the REIMS data present challenges for machine learning models. This thesis aims to develop robust, uncertainty-aware and generalizable machine learning cancer classification models for the iKnife. To address the challenges of heterogeneity and coarse labels, the thesis explores uncertainty estimation and self-supervised learning. We apply uncertainty estimation to REIMS data and analyze the uncertainty calibration of the models as well as their computational cost. We also pre-train self supervised deep networks on Basal Cell Carcinoma data and fine-tune the network on breast data, combining self supervised learning with uncertainty estimation.