StyleX-Med

Hybrid Geometric-GAN Framework for Cephalometric Profile Transformation

Identified fundamental GAN limitation: Demonstrated that StyleGAN latent editing achieves only 0.6-9.4px chin movement vs. clinically required 15-25px through systematic ablation across W space (9.4px), W+ space (1.4px), and StyleX channels (0.8px)

Developed novel hybrid architecture combining RBF Thin Plate Spline warping for geometric control (15-25px precise chin movement) with StyleGAN2 refinement for photorealism, explicitly separating structural transformation from learned visual refinement

Designed landmark-constrained GAN inversion with 50× weighted geometric loss (compensating for 100× magnitude difference between pixel and landmark losses), achieving <3px drift on fixed landmarks while enabling 10-11px chin transformations

Trained ViT-B/16 classifier (89.2% validation accuracy) and landmark regressor for adaptive amplification control, integrating confidence-based warping strength selection (1.8-4.5× range) to handle variable baseline convexity

Overcame severe dataset imbalance (63% CONVEX, 7% STRAIGHT training data) through multi-objective GAN regularization: StyleX classifier loss (0.5×), landmark consistency loss (2.0×), and diversity penalty (0.1×), achieving balanced 31/31/31% generation distribution

Achieved 80% visual success rate for CONVEX→STRAIGHT transformations with photorealistic output and anatomical validity, establishing proof-of-concept for orthodontic treatment visualization and patient communication