Digital Image Acquisition Using a Consumer-Type Digital Camera in the Anatomic Pathology Setting
Bibliographic record
Abstract
Imaging is central to anatomic pathology. The captured images are used for documentation, archiving, teaching, and publication. The advent of low-cost, consumer-type, high-end digital cameras has provided a convenient, easy-to-use alternative for routine image acquisition. The various applications for digital image acquisition in anatomic pathology include, among others, digitizing conventional photographs, digital gross photography and digital macrophotography, digitizing radiographic images, and digital photomicrography. This article reviews digital image acquisition in the anatomic pathology setting using a consumer-type digital camera. The camera type chosen as an example for the discussion was selected for its popularity and wide use among pathologists and for its potential to function as a sole image input device in all applications combined. Techniques and accessories to further increase the functionality of the camera and help overcome some of the commonly encountered problems in some applications are described.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".