Selection of optimal texture descriptors for retrieving ultrasound medical images
Bibliographic record
Abstract
Although feature selection has been proven to be very effective in machine learning and pattern classification applications, it has not been widely practiced in the area of image annotation and retrieval. This paper presents a method of selecting a near optimal to optimal subset of statistical texture descriptors in efficient representation and retrieval of ultrasound medical images. An objective function combining the concept of between-class distance and within-class divergence among the training dataset has been proposed as the evaluation criteria of optimality. Searching for the selection of optimal subset of image descriptors has been performed using Multi-Objective Genetic Algorithm (MOGA). The proposed feature selection based approach of image annotation and retrieval has been tested using a database of 679 ultrasound ovarian images and satisfactory retrieval performance has been achieved. Besides, performance of ultrasound medical image retrieval with and without applying feature selection based image annotation technique has also been compared.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".