Model-based multiple active contours matching for radiographic images
Bibliographic record
Abstract
Medical images are noisy and complex. Segmentation and labeling of X-ray images represent many difficulties. Active contours have become an attractive subject in computer vision. Connectivity and closure properties of these contours help to overcome some important difficulties in computer vision, as edge organization and region merging. Consequently, using deformable contours reduces dramatically the search space dimension. In this paper, we present a model-based approach of multiple dynamic non-parametric curves matching with X-ray images. The model is formed of three parts: (i) image formation, (ii) high-level interaction, and (iii) contours smoothing constraints. The first and second part measure consistency of the reconstructed object with the given image and the relational a priori information of the object, respectively. The scene model represents a hierarchical structure of three processes: lines, regions and relational graphs. An object is modeled as a set of linked subobjects according to a 3-D relational graph which can be projected from a known viewpoint in a 2-D region relational graph. The resultant function is optimized using a descending search method with randomized sampling. Finally, successful results are presented for object matching in semitransparent noisy synthetic scenes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".