Title: MAGIC-Flow: Multiscale Adaptive Conditional Flows for Generation and Interpretable Classification
Primary area: generative models
Keywords: Conditional normalizing flows; Invertible generative models; Joint generation and classification; Medical imaging; Explainability and likelihood visualization; Data augmentation; Trustworthy and generalizable AI;

Abstract: Generative modeling has emerged as a powerful paradigm for representation learning, but its direct applicability to challenging fields like medical imaging remains limited: mere generation, without task alignment, fails to provide a robust foundation for clinical use. We propose MAGIC-Flow, a conditional multiscale normalizing flow architecture that performs generation and classification within a single modular framework. The model is built as a hierarchy of invertible and differentiable bijections, where the Jacobian determinant factorizes across sub-transformations. We show how this ensures exact likelihood computation and stable optimization, while invertibility enables explicit visualization of sample likelihoods, providing an interpretable lens into the model’s reasoning. By conditioning on class labels, MAGIC-Flow supports controllable sample synthesis and principled class-probability estimation, effectively aiding both generative and discriminative objectives.
We evaluate MAGIC-Flow against top baselines using metrics for similarity, fidelity, and diversity. Across multiple datasets, it addresses generation and classification under scanner noise, and modality-specific synthesis and identification. Results show MAGIC-Flow creates realistic, diverse samples and improves classification.
MAGIC-Flow is an effective strategy for generation and classification in data-limited domains, with direct benefits for privacy-preserving augmentation, robust generalization, and trustworthy medical AI.

TL;DR: We propose MAGIC-Flow, an invertible conditional normalizing flow that jointly learns generation and classification, improving both performance and explainability in medical imaging through exact likelihoods.