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 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".