A Statistical Focusing Metric for Fluorescent Microscopy
Why this work is in the frame
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Bibliographic record
Abstract
Autofocusing has critical importance for imaging systems with motorized microscopes in healthcare. It is not possible to diagnose on a picture that captured out of focus. In the literature, it has been observed that there are limited studies on focusing methods specific to fluorescent microscopes. In this study, a focus metric is proposed special to fluorescent microscope images. The proposed metric evaluates the responses generated by gradient filters of varying kernel size at a pixel point, and takes into account their standard deviations. The proposed method was tested on lung and breast tissue samples obtained with fluorescent microscope, and experimental results were reported. It is shown that the developed method overperforms the local gradient filters and produces an average error of 0.16 levels.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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 it