Tumour Ellipsification in Ultrasound Images for Treatment Prediction in\n Breast Cancer
Bibliographic record
Abstract
Recent advances in using quantitative ultrasound (QUS) methods have provided\na promising framework to non-invasively and inexpensively monitor or predict\nthe effectiveness of therapeutic cancer responses. One of the earliest steps in\nusing QUS methods is contouring a region of interest (ROI) inside the tumour in\nultrasound B-mode images. While manual segmentation is a very time-consuming\nand tedious task for human experts, auto-contouring is also an extremely\ndifficult task for computers due to the poor quality of ultrasound B-mode\nimages. However, for the purpose of cancer response prediction, a rough\nboundary of the tumour as an ROI is only needed. In this research, a\nsemi-automated tumour localization approach is proposed for ROI estimation in\nultrasound B-mode images acquired from patients with locally advanced breast\ncancer (LABC). The proposed approach comprised several modules, including 1)\nfeature extraction using keypoint descriptors, 2) augmenting the feature\ndescriptors with the distance of the keypoints to the user-input pixel as the\ncentre of the tumour, 3) supervised learning using a support vector machine\n(SVM) to classify keypoints as "tumour" or "non-tumour", and 4) computation of\nan ellipse as an outline of the ROI representing the tumour. Experiments with\n33 B-mode images from 10 LABC patients yielded promising results with an\naccuracy of 76.7% based on the Dice coefficient performance measure. The\nresults demonstrated that the proposed method can potentially be used as the\nfirst stage in a computer-assisted cancer response prediction system for\nsemi-automated contouring of breast tumours.\n
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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 | 0.002 |
| 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) | 0.001 | 0.001 |
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".