Focus Quality Assessment of High-Throughput Whole Slide Imaging in\n Digital Pathology
Bibliographic record
Abstract
One of the challenges facing the adoption of digital pathology workflows for\nclinical use is the need for automated quality control. As the scanners\nsometimes determine focus inaccurately, the resultant image blur deteriorates\nthe scanned slide to the point of being unusable. Also, the scanned slide\nimages tend to be extremely large when scanned at greater or equal 20X image\nresolution. Hence, for digital pathology to be clinically useful, it is\nnecessary to use computational tools to quickly and accurately quantify the\nimage focus quality and determine whether an image needs to be re-scanned. We\npropose a no-reference focus quality assessment metric specifically for digital\npathology images, that operates by using a sum of even-derivative filter bases\nto synthesize a human visual system-like kernel, which is modeled as the\ninverse of the lens' point spread function. This kernel is then applied to a\ndigital pathology image to modify high-frequency image information deteriorated\nby the scanner's optics and quantify the focus quality at the patch level. We\nshow in several experiments that our method correlates better with ground-truth\n$z$-level data than other methods, and is more computationally efficient. We\nalso extend our method to generate a local slide-level focus quality heatmap,\nwhich can be used for automated slide quality control, and demonstrate the\nutility of our method for clinical scan quality control by comparison with\nsubjective slide quality scores.\n
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".