MAGIC-Flow: Multiscale Adaptive Conditional Flows for Generation and Interpretable Classification
This addresses the need for robust, interpretable AI in data-limited domains like medical imaging, offering benefits for privacy-preserving augmentation and trustworthy clinical use, though it appears incremental as it builds on existing normalizing flow methods.
The paper tackles the problem of applying generative modeling to medical imaging by proposing MAGIC-Flow, a conditional multiscale normalizing flow architecture that integrates generation and classification in a single framework, resulting in realistic, diverse samples and improved classification across multiple datasets.
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.