Bibliographic record
Abstract
The work in this paper examines and criticizes different methods used for prostate tissue classification using trans-rectal ultra-sound (TRUS) images. The suspicious regions are first identified by an accurate region of interest (ROI) identification algorithm. The identified ROIs' are then analyzed using statistical based features as well as spectral based features. For the statistical based features each ROI is treated as an image and different statistical features are constructed from the ROI image. The statistical feature set is constructed from the grey level difference vector (GLDV), as well as the grey level dependence matrix (GLDM). While for the spectral based features, all of the ROIs' pixels are aligned to form a ROI I-D signal. Different spectral features are then constructed from the I-D ROI signals. The spectral feature set is constructed using geometrical features extracted from the estimated power spectrum density (PSD) as well as the estimation of signal parameters via rotational invariance technique (ESPRIT) features. A classifier based feature selection algorithm using ants colony optimization (ACO), a recently proposed optimization technique is adopted and used to select an optimal subset from each of the above extracted features. The obtained accuracy ranges from 72.2% to 93.75% using a Support Vector Machine classifier.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".