CI Slide: calibration slide for quantitative microscopy imaging in absorbance
Bibliographic record
Abstract
New imaging technologies are changing the field of digital pathology. This field faces numerous challenges and there is a pressing need for standardization, calibration protocols, quality control and quantitative assessment. We have designed a new calibration imaging slide (Cancer Imaging Slide), specifically to measure the characteristics of old or new imaging systems or scanners. The layout of the slide consists of 138 boxes with the side length of 1.6 mm, containing objects of known morphologic and photometric characteristics. Among them, 112 boxes contain different permutations of circles, ovals, and squares. The circles have different radii, radius/pitch ratios and step transmissions. The ovals have different sizes and orientations. The squares are consistent in size and orientation but have different step transmission values. Also, 16 boxes contain three resolution test targets: crosses, USAF target and Siemens star. The last 10 boxes are blank boxes with different transmission values. Four slides were scanned and imaged on one commercial whole-slide scanner and one high resolution imaging system. After segmenting the images, about 200 features (photometric, morphologic and architectural) were measured with our in-house image processing software. The objective of the project is to develop a statistical process control using this new slide. In this paper, we describe the characteristics of the slide and present our preliminary results.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.025 |
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".