Craniofacial Volumetric Image Estimation From a Lateral Cephalogram Using Cross-Dimensional Discrete Embedding Mapping
Bibliographic record
Abstract
The lateral cephalogram (LC)-based volumetric image estimation is feasible to relieve the hazardous radiation exposure and study patient-specific 3D morphology of craniofacial structures in clinical orthodontics. The deep learning-based approach has potential in volumetric reconstruction of computed tomography in recent years. However, existing work employed the cross-dimensional feature transformation by channel concatenation or element rearrangement, without considering the voxel-wise semantic inference regarding a variety of anatomical tissues. The deep learning-based model relied on synthetic paired 2D X-rays and 3D volumes and required an additional domain adaption module to generalize to clinical data. This work customizes a cross-dimensional discrete embedding mapping model (CD$^{2}$EM) for 3D craniofacial volumetric image estimation from a 2D LC. The vector quantization-based discrete embedding and the learnable codebook are introduced to relieve redundancy in feature representation for voxel-wise inference of craniofacial structures, with codes indicating the probability distribution of a variety of anatomical tissues. We devise an unsupervised learning scheme to generalize the model to clinically obtained LCs. We demonstrate the advantage and effectiveness of the discrete coding and mapping scheme on the clinical LC-based voxel-wise craniofacial volumetric image estimation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".