CL-GAN: A progressive curriculum learning approach for bone CT super-resolution
Bibliographic record
Abstract
Cone-beam computed tomography (CBCT) provides rapid, low-dose imaging with large anatomical coverage but lacks the resolution required for detailed bone assessment in rheumatoid arthritis (RA). Enhancing CBCT to match high-resolution peripheral quantitative CT (HR-pQCT) is challenging due to differences in resolution, noise, and artifact profiles, causing standard super-resolution approaches to struggle in training with instability. We propose CL-GAN (curriculum learning generative adversarial network) to train a Cycle-Consistent GAN for CBCT super-resolution. Training is structured into four stages, beginning with paired synthetic mappings and gradually introducing unpaired real CBCT and HR-pQCT images from healthy and RA-affected joints. Each stage progressively increases task difficulty to bridge domain gaps stably. Performance was evaluated using image quality metrics, trabecular bone morphometry, and blinded review by an experienced reviewer. Repeated measures ANOVA and post-hoc paired t-tests assessed significance across stages. Progressive training led to consistent improvements in image quality and trabecular bone metric accuracy, with all gains statistically significant (p<0.001). In blinded review, two reviewers detected all RA erosions while one missed one erosion, and all reviewers reported difficulty distinguishing enhanced CBCT from HR-pQCT. An ablation study showed that skipping curriculum stages impaired image quality, highlighting the importance of gradual domain adaptation. This study demonstrates that curriculum learning enables stable and effective training of a super-resolution algorithm for CBCT images in the context of RA. By enhancing image quality and structural interpretability, the proposed framework increases the amount of relevant information that can be extracted from CBCT, supporting its utility in RA analysis and research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".