When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation

arXiv:2609.29387 · cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.CV

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/vboussot/Synthrad2025

Terminology

• Consistency — 24× in this paper · explained in 3 episodes
• Alignment — 19× in this paper · explained in 1 episode
• Benchmark — 18× in this paper · explained in 2 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• AND GAMMA PASS RATE — 3× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Benchmarking — 2× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 10 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Segment Anything Model — 1× in this paper
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging
• Eliminating Registration Bias in Synthetic CT Generation: A Physics-Based Simulation Framework
• Deformation-aware GAN for Medical Image Synthesis with Substantially Misaligned Pairs
• IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration
• SAM 2: Segment Anything in Images and Videos
• Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration
• KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imaging

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