Quality Control of Whole Slide Images using the YOLO Concept
Bibliographic record
Abstract
Computational pathology applies computer vision algorithms on whole slide images. The digitization of tissue glass slides marks a significant change in the clinical diagnostic workflow. One of the challenges in digital pathology is the presence of artifacts such as tissue fold, air bubbles, and ink-markers on archived cases. These artifacts may affect the focus points in digital scanners, and their presence may negatively affect the quality of the output tissue image and the subsequent diagnosis. Manual review of whole slide images requires experts, and it is a laborious and time-consuming task. In this paper, we trained the YOLO-v4 (You-Only-Look-Once) model to detect air bubble edges, tissue folds, which can happen during slide preparation, and ink-marked tissue glass slides, which occur when pathologists highlight regions of interest on glass slides. Our method is not only fast but also highly accurate. The experiments showed 99.5 % IOU calculation (intersection over union, also called Jaccard Index) for locating artifacts.
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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 | 0.004 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".