Multiresolution Analysis and Classification of Small Bowel Medical Images
Why this work is in the frame
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Bibliographic record
Abstract
This is the first reported work in the area of small bowel image classification and a novel analysis system was developed. Principles of human texture perception were used to design features which can discriminate between abnormal and normal images. The proposed method extracts statistical features from the wavelet domain, which describe the homogeneity of localized areas within the small bowel images. To ensure that robust features were extracted, a shift-invariant discrete wavelet transform (SIDWT) was explored. LDA classification was used with the leave one out method to improve classification under the small database scenario. A total of 75 abnormal and normal bowel images were used for experimentation resulting in high classification rates: 85% specificity and 85% sensitivity. The success of the system can be accounted to the discriminatory and robust feature set (translation, scale and semi-rotational invariant), which successfully classified various sizes and types of pathologies at multiple viewing angles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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