Autofocusing for Automated Microscopic Evaluation of Blood Smear and Pap Smear
Why this work is in the frame
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Bibliographic record
Abstract
Autofocusing is a fundamental procedure towards automated microscopic evaluation of blood smear and pap smear samples for clinical diagnosis. This paper presents comparison results of 16 focus algorithms based on a total of 8000 bright-field images from 10 blood smear and pap smear samples. A ranking methodology adapted from our previously proposed ranking system is used for thoroughly evaluating the performance of the selected 16 focus algorithms. Experimental results demonstrate that the variance algorithm provides the best overall performance, which will be selected for our future implementation of an automated microscopic system for computer-assisted blood smear and pap smear evaluation. Together with our previously reported findings, we hypothesize that the variance algorithm or the Normalized variance algorithm is the optimal focus algorithm for all non- fluorescence microscopy applications.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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