A Novel Approach for Precise Tissue Tracking in Breast Lumpectomy
Bibliographic record
Abstract
One of the most common cancers among women is breast cancer which can be treated surgically in the early stages with a lumpectomy technique. In the context of breast lumpectomy procedures, accurately tracking tumours presents a critical challenge worsened by various sources of anatomical deformations, including breathing, tissue cutting, and ultrasound probe pressure. To address this, we explore how a realistic tissue deformation simulator can enhance the precision of locating internal targets by accurately assessing the deformation applied to a preoperative model of the breast, considering the distinct mechanical properties of both the breast tissue and the tumour within it. Our method uses advanced artificial intelligence techniques by combining a generative variation autoencoder (GNN-VAE) and an updating method called ensemble smoother with multiple data assimilation (ES-MDA), creating a dynamic model based exclusively on surface node data to update all nodes within the tissue. By leveraging a realistic tissue deformation simulator, our approach uses breast surface tracking to infer full tissue deformations. This makes the method compatible with various simulation tools and suitable for tissues with complex properties. The results indicate that the trained network has an accuracy of 0.014 cm with training data, and 0.026 cm with the testing portion of data, demonstrating precision in tumour localization and significantly improving upon current methods. This innovation can potentially enhance patient outcomes by making breast cancer surgery safer, less invasive, and more efficient.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".