IFC-HFlowVAE: A self-enhancing generative framework with structural anchoring for imbalanced clinical data augmentation.

Authors

Lu Yuwen, Shuyu Chen

Published in

PloS one. Volume 21. Issue 9. Pages e0357260. Epub Sep 03, 2026.

Abstract

Class imbalance and limited minority-class samples remain major challenges for developing reliable clinical diagnostic models, as insufficient observations often fail to capture complex minority-class distributions. Existing augmentation methods either rely on heuristic interpolation or suffer from instability when learning sparse and heterogeneous medical tabular data. This study proposes IFC-HFlowVAE, an iterative feedback and consensus framework built upon a flow-enhanced heterogeneous variational autoencoder for minority-class data generation. The proposed framework first models mixed-type clinical attributes through HFlowVAE and then introduces a self-circulating refinement strategy to progressively improve generated samples. To alleviate degradation during iterative refinement, SMOTE-generated samples are incorporated as structural references, followed by a localized GMM-based refinement procedure that guides samples toward more representative minority-class regions. Comprehensive experiments on seven clinical tabular datasets demonstrate that IFC-HFlowVAE improves downstream classification performance compared with existing augmentation approaches. Furthermore, fidelity analyses show that the proposed framework better preserves challenging minority-class characteristics, particularly skewed and multimodal numerical distributions.

PMID:
42691146
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.

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