Craniofacial Volumetric Image Estimation From a Lateral Cephalogram Using Cross-Dimensional Discrete Embedding Mapping
Why this work is in the frame
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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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> 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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it