Healthy Counterfactual Generation via Diffusion Inpainting for Mammography Classification

arXiv:2610.12147 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/ines03garcia/diffusion-based-counterfactual-generation

Terminology

Diffusion Model — 9× in this paper · explained in 8 episodes
Data Augmentation — 7× in this paper · explained in 7 episodes
Diffusion Models — 7× in this paper · explained in 6 episodes
AUC — 5× in this paper · explained in 2 episodes
Denoising Diffusion Probabilistic Model — 4× in this paper
Fidelity — 4× in this paper · explained in 2 episodes
Accuracy — 3× in this paper · explained in 1 episode
Consistency — 3× in this paper · explained in 3 episodes
Transformer — 3× in this paper · explained in 2 episodes
Conference on Computer Vision and Pattern Recognition — 2× in this paper
Robustness — 2× in this paper · explained in 5 episodes
AdamW Optimizer — 1× in this paper · explained in 1 episode
Aggregation — 1× in this paper · explained in 1 episode
Benchmark — 1× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
Explainability — 1× in this paper · explained in 3 episodes
Flow Matching — 1× in this paper · explained in 8 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Generalization — 1× in this paper · explained in 13 episodes
Generative Model — 1× in this paper · explained in 2 episodes
Generative Models — 1× in this paper · explained in 2 episodes

Sources

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

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