Bibliographic record
Abstract
Breast cancer is the most common cancer occurring in women. Breast-conserving surgery is a desirable choice for an early-stage breast cancer. An intra-operative margin assessment of excised breast lesion tissue can help avoid additional surgeries. An essential problem in intra-operative margin assessment is how to extract an accurate boundary of the excised lesion automatically and quickly. To solve this problem, we segment breast cancer ultrasound (US) images and then generate boundaries based on the segmentation results. In this research, we propose a new convolutional neural network model, named IU-Net, to segment breast cancer US images. IU-Net combines inception blocks and the well-known U-Net model. We train IU-Net with US images and corresponding manually segmented images provided by Dr. Jeffery Carson and his research group of the Lawson Health Research Institute, London, Ontario, Canada. We also apply an autoencoder in training IU-Net. The experimental results show that IU-Net achieves slightly more accurate results than U-Net and uses 3.8x fewer parameters than U-Net.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".