Spatial Normalization of Human Back Images for Dermatological Studies
Bibliographic record
Abstract
A large number of pigmented skin lesions (PSLs) are a strong predictor of malignant melanoma. Many dermatologists advocate total body photography for high-risk patients because detecting new-appearing, disappearing, and changing PSL is important for early detection of the disease. However, manual inspection and matching of PSL is a subjective, tedious, and error-prone task. A computer program for tracking the corresponding PSL will greatly improve the matching process. In this paper, we describe the construction of the first human back template (atlas), which is used to facilitate spatial normalization of the PSL during the matching process. Four pairs of anatomically meaningful landmarks (neck, shoulder, armpit, and hip points) are used as reference points on the back image. Using the landmarks, a grid with longitudes and latitudes is constructed and overlaid on each subject-specific back image. To perform spatial normalization, the grid is registered into the back template, a unit-square rectilinear grid. To demonstrate the benefits of using the back template, we apply several state-of-the-art point-matching algorithms on 56 pairs of real dermatological images and show that utilizing spatially normalized coordinates improves the PSL matching accuracies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".