A New Method to Collapse the S-Transform Into Local Spectra by Integrating in Squares
Bibliographic record
Abstract
The two-dimensional S-transform (2D-ST) is a promising technique for identifying texture characteristics of brain pathology in magnetic resonance images. Previous work has obtained "texture curves" from the four-dimensional ST domain by integrating the local Fourier domain for each pixel in concentric rings of constant width. Using this approach, previous studies have shown that specific spatial frequency bands can discriminate between active and inactive lesions in multiple sclerosis and between brain tumor genotypes. However, integration in rings produces an artificial drop in spectral power at the Nyquist frequency, potentially masking true high-frequency information. We present a new method of producing texture curves by integrating the ST domain in squares. We compare the two methods on synthetic and clinical multiple sclerosis data and show that our method is simple to implement and produces spectra localized to frequencies below the Nyquist frequency. Integration in squares may produce spectra that are more sensitive to subtle high-frequency changes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.007 | 0.003 |
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".