Self-supervised learning and uncertainty estimation for surgical margin detection with mass spectrometry
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".