Design and characterization of a digital image acquisition system for whole-specimen breast histopathology
Bibliographic record
Abstract
We have developed a digital histopathology imaging system capable of producing a three-dimensional (3D) representation of histopathology from an entire lumpectomy specimen. The system has the potential to improve the accuracy of surgical margin assessment in the treatment of breast cancer by providing finer sampling and 3D visualization. A scanning light microscope was modified to allow digital photomicrography of a stack of large (up to 120x170 mm2) histology slides cut serially through the entire specimen. The images are registered and displayed in 2D and 3D. The design of the system, which reduces or eliminates the appearance of 'tiling' and 'seam' artefacts inherent in the scanning method, is described and its resolution, contrast/noise and coverage properties are characterized through measurements of the modulation transfer function (MTF), depth of field (DOF) and signal difference to noise ratio (SDNR). The imaging task requires a lateral resolution of 5 microm, an SDNR of 5 between relevant features, 'tiling artefact' at a level below the detectability threshold of the eye, and 'seam artefact' of less than 5-10 microm. The tests demonstrate that the system is largely adequate for the imaging task, although further optimizations are required to reduce the degradation of coverage incurred by seam artefact.
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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 | 0.000 | 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 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".