Classification of breast tissue using a laboratory system for small-angle x-ray scattering (SAXS)
Bibliographic record
Abstract
Structural changes in breast tissue at the nanometre scale have been shown to differentiate between tissue types using synchrotron SAXS techniques. Classification of breast tissues using information acquired from a laboratory SAXS camera source could possibly provide a means of adopting SAXS as a viable diagnostic procedure. Tissue samples were obtained from surgical waste from 66 patients and structural components of the tissues were examined between q = 0.25 and 2.3 nm(-1). Principal component analysis showed that the amplitude of the fifth-order axial Bragg peak, the magnitude of the integrated intensity and the full-width at half-maximum of the fat peak were significantly different between tissue types. A discriminant analysis showed that excellent classification can be achieved; however, only 30% of the tissue samples provided the 16 variables required for classification. This suggests that the presence of disease is represented by a combination of factors, rather than one specific trait. A closer examination of the amorphous scattering intensity showed not only a trend of increased scattering intensity with disease severity, but also a corresponding decrease in the size of the scatterers contributing to this intensity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".