An efficient shape based feature for retrieval of healthcare literatures using CBIR technique
Bibliographic record
Abstract
Recent advances in healthcare such as Evidence Based Medicine (EBM) and Clinical Decision Support Systems (CDSS) requires practitioners to frequently access archived historical healthcare literatures and images. As the majority of healthcare literatures contain images such as medical images, clip arts, waveforms, flow charts and block diagrams, in this paper we present the use of Content Based Image Retrieval (CBIR) for efficient healthcare literature search and retrieval. We introduce a novel shape based feature called Fourier Edge Orientation Autocorrelogram (FEOAC) for search and retrieval of healthcare literatures. Scale and translation invariant Edge Orientation Autocorrelogram (EOAC) feature is made rotation invariant by applying Fourier transform. This Fourier based shape feature also reduces the feature set dimension enabling faster retrieval of document images in large databases. Experimental results show that FEOAC outperforms EOAC for search and retrieval of healthcare document images, with improved precision and recall rates.
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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 | 0.000 | 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 | 0.001 | 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 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".