Interleaved Fusion Learning for Trustworthy AI: Improving Cross-Dataset Performance in Cervical Cancer Analysis

Abstract

This study introduces a novel Interleaved Fusion Learning (IFL) methodology leveraging transfer learning to generate a family of models optimized for specific datasets while maintaining superior generalization performance across others. The approach is demonstrated in cervical cancer screening, where cytology image datasets present challenges of heterogeneity and imbalance. By interleaving transfer steps across dataset partitions and regulating adaptation through a dynamic learning parameter, IFL promotes both domain-specific accuracy and cross-domain robustness. To evaluate its effectiveness, complementary metrics are used to capture not only predictive accuracy but also fairness in performance distribution across datasets. Results highlight the potential of IFL to deliver reliable and unbiased models in clinical decision support. Beyond cervical cytology, the methodology is designed to be scalable to other medical imaging tasks and, more broadly, to domains requiring equitable AI solutions across multiple heterogeneous datasets.

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