An automated framework for counting lymphocytes from microscopic images
Bibliographic record
Abstract
The problem of cell counting is of tremendous importance in many clinical laboratory and pathology testing procedures, where an accurate cell count is vital for various diagnostic objectives, such as classifying disease developments and assessing drug effectiveness. Unfortunately, even today, this task remains mostly a manual endeavor, and depends on time-consuming human labor. Automated cell counting is clearly a desirable alternative, but has yet to fully emerge due to outstanding challenges and limitations in equipment cost and processing methods. To this end, a framework for counting lymphocytes from microscopic images is proposed in this paper. The framework requires modest equipment investment, consisting mostly of a standard microscope and a digital camera, while offering promising performance results. This is possible due to a robust image processing pipeline involved, with strategically designed color space, filtering and edge detection operations. The framework is validated on the publicly available ALL-IDB dataset, which is noted for its challenging nature due to various obstacles for image processing operations. Specifically, this work tackles the automated counting of lymphocytes from this dataset. Currently, the proposed framework is already capable of delivering accuracies of over 90%, with low computational complexity and execution time. Therefore, with further performance improvements, it should be expected to offer a compelling alternative for clinical testing procedures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".