Automated breast ultrasound features associated with diagnostic performance of a multiview convolutional neural network according to the level of experience of radiologists
Why this work is in the frame
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Bibliographic record
Abstract
Abstract To investigate automated breast ultrasound (ABUS) features affecting the use of a multiview convolutional neural network (CNN) for breast lesions according to the level of experience of radiologists. A total of 656 breast lesions (152 malignant and 504 benign lesions) were included and reviewed by 6 radiologists for background echotexture, glandular tissue component (GTC), and lesion type and size without as well as with a multiview CNN. The sensitivity, specificity, and the area under the receiver operating curve (AUC) for ABUS features were compared between 2 sessions according to the level of the radiologists’ experience. Radiology residents showed significant AUC improvement with the multiview CNN for mass (0.81–0.91, P=0.003) and non-mass lesions (0.56–0.90, P=0.007), all background echotextures (homogeneous-fat: 0.84–0.94, P=0.04; homogeneous-fibroglandular: 0.85–0.93, P=0.01; heterogeneous: 0.68–0.88, P=0.002), all GTC levels (minimal: 0.86–0.93, P=0.001; mild: 0.82–0.94, P=0.003; moderate: 0.75–0.88, P=0.01; marked: 0.68–0.89, P<0.001), and lesions ≤10mm (≤5mm: 0.69–0.86, P<0.001; 6–10mm: 0.83–0.92, P<0.001). Breast specialists showed significant AUC improvement with the multiview CNN in heterogeneous echotexture (0.90–0.95, P=0.03), marked GTC (0.88–0.95, P<0.001), and lesions ≤10mm (≤5mm: 0.89–0.93, P=0.02; 6–10mm: 0.95–0.98, P=0.01). With the multiview CNN, ABUS performance among radiology residents was improved regardless of lesion type, background echotexture, or GTC. For breast lesions smaller than 10mm, both radiology residents and breast specialists achieved better ABUS performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it