Photogrammetry: Adding Another Dimension to Virtual Gross Pathology Teaching
Bibliographic record
Abstract
Pathology is a discipline that relies on the description and interpretation of changes occurring in organs and tissues, and it is largely a "hands-on" experience, both during training and professional practice. Instigated by the need to provide a solution for online learning and teaching, a plethora of different approaches have been tested during the Covid-19 pandemic. The enforced inability to meet in person created the necessity to quickly replace the hands-on experience of practical classes, routinely considered the "gold standard" in undergraduate pathology teaching, with alternative and innovative digital solutions that could allow the students to appreciate most, if not all, features of the specimen to describe and interpret. Here we present a successful deployment of photogrammetry for the purpose of teaching gross veterinary pathology to undergraduate students. Fresh specimens obtained during routine diagnostic post-mortem activity have been photographed using Digital Single-Lens Reflex cameras and rendered into high quality 3D models, preserving almost unaltered morphology, color, and texture, when compared to the original specimen. Once processed using photogrammetry software, exported and uploaded into an online repository, 3D models become readily available via our digital learning platform (CANVAS) to all undergraduate students for self-study and consolidation, as well as to teaching staff for use during online lectures, traditional face-to-face classes, small group teaching and seminars. Preliminary data collected from students' feedback highlighted the positive reception from users, and the enriched learning experience, while prolonging indefinitely the availability of rare and perishable teaching material.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".