High Throughput Algorithm for Leukemia Cell Population Statistics on a Hemocytometer
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Bibliographic record
Abstract
This paper presents a high throughput cell count and cluster classification algorithm to quantify population statistics of leukemia cell lines on a conventional hemocytometer. The algorithm has been designed, implemented and tested on test images that vary in image quality. The proposed algorithm uses a recursively segmented, median filtered and a boosted Prewitt gradient mask to generate a boundary box that encloses all the identified cells. Intensity profile plots acting as signature plots further assist in classifying a single isolated cell from a cell cluster. Processed results compared manually by a biological expert resulted in an accuracy of 95 % for even low quality images with a computational time ranging between 8-12sec. Improved performance from the proposed algorithm could be observed when compared with other conventional image analysis tools.
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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 | 0.001 |
| 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