A Fast Technique for White Blood Cells Nuclei Automatic Segmentation Based on Gram-Schmidt Orthogonalization
Why this work is in the frame
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Bibliographic record
Abstract
Blood testing is one of the most important clinical examinations. Counting different blood cells is a significant process in a clinical laboratory. Manual microscopic evaluation is compulsory in case there is suspicious abnormality in the blood sample. Yet, the manual inspection is time-consuming and requires adequate technical knowledge. Therefore, automatic medical diagnosis systems are necessary to help physicians to diagnose diseases in a fast and nonetheless competent way. Cell automatic classification has wider interest especially for clinics and laboratories. Segmentation is the most important step for automatic classification success. This paper represents an efficient technique for automatic blood cell nuclei segmentation. This technique is relying on enhancing the color of the target object, nucleus, and filtering the image. Small objects are eliminated employing morphological operations. A set of 365 blood images was used to quantitatively evaluate this segmentation technique. Assessment of the proposed technique on the blood image set gives 85.4% accuracy. In comparison to other published technique that was implemented and executed on the same dataset, the proposed segmentation technique performance was found to be superior. A differential segmentation performance evaluation was performed on the five normal white blood cell types to compare isolated performance. Eosin Phil was found to have the highest segmentation accuracy with 90.1%. Lymphocyte and Basophil have the lowest accuracy with 78.3% and 78.6% respectively. The blood images dataset and the source code are published on MATLAB file exchange website for comparison and re-production.
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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