A new approach to ultrasonic detection of malignant breast tumors
Bibliographic record
Abstract
In this work, we report the use of ultrasound RF time series analysis for separating benign and malignant breast lesions with similar B-mode appearance. The RF time series method is versatile and requires only a few seconds of imaging. We have employed the spectral and fractal features of ultrasound RF time series and used support vector machines with leave-one-patient-out cross validation of the classification. We have also produced cancer probability maps, by estimating the posterior malignancy probability of regions of size 1 mm2 in the suspicious lesions. The first 12 patient cases of our ongoing study are reported here. Pathologic analysis of the cores using ultrasound guided needle biopsy confirmed the tissue type. We report an area under receiver operating characteristic curve of 0.82. We were able to successfully classify 6 out of 7 patients with malignant breast lesions and 4 out of 5 patients with benign lesions, with success defined as correct classification of at least 75% of the 1 mm2 regions in the area of the lesion. The above findings suggest that ultrasound time series along with support vector machines can help in differentiating malignant from benign breast lesions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".