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🖥 3D Synthetic Medical Image Generation (GAN)

The Generator and Discriminator render as genuine layered 3D neural-network stacks orbiting in real WebGL space — the same 5-stage adversarial training pipeline as the 2D original, now viewable in depth from any angle.

Multimodal Imaging Fusion & AI Triage3DAdvanced60 FPS📱 Mobile-adapted

⚙ Under the hood

The 2D original's Generator/Discriminator node diagrams, quality-scored image thumbnails and stage-driven metrics are ported 1:1 into real Three.js/WebGL space: each network is a genuine layered 3D stack — nodes positioned across true Z-depth per layer, not a flat on-screen graph — so orbiting the scene actually reveals its architecture. Generator Complexity now controls how many 3D layers are built (4/6/9, approximating the shallow/medium/deep-StyleGAN labels), Input Noise Level drives a real 3D particle stream drifting toward the generator, and Training Speed scales the animation tempo. The same five pipeline stages — dataset sampling, generator synthesis, discriminator evaluation, adversarial tug-of-war, and validation merge — drive identical metrics (images generated, discriminator accuracy, FID, epoch) and the same real/fake verdict-ring logic on the image thumbnails, now rendered as textured planes in 3D.

Three.jsBiomedicineAI & ML

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install