Chika F. Ezeana | Searcharxiv

arXiv2026

Revisiting Invariant Learning for Out-of-Domain Generalization on Multi-Site Mammogram Datasets

Achieving health equity in Artificial Intelligence (AI) requires diagnostic models that maintain reliability across diverse populations. However, breast cancer screening systems frequently suffer from domain overfitting, degrading significantly when deployed to varying demographics. While Invariant Learning algorithms aim to mitigate this by suppressing site-specific correlations, their efficacy in medical imaging remains underexplored. This study comprehensively evaluates domain generalization techniques for mammography. We constructed a multi-source training environment aggregating datasets from the United States (CBIS-DDSM, EMBED), Portugal (INbreast, BCDR), and Cyprus (BMCD). To assess global generalizability, we evaluated performance on unseen cohorts from Egypt (CDD-CESM) and Sweden (CSAW-CC). We benchmarked Invariant Risk Minimization (IRM) and Variance Risk Extrapolation (VREx) against a rigorously optimized Empirical Risk Minimization (ERM) baseline. Contrary to expectations, standard ERM consistently outperformed specialized invariant mechanisms on out-of-domain testing. While VREx showed potential in stabilizing attention maps, invariant objectives proved unstable and prone to underfitting. We conclude that engineering equitable AI is currently best served by maximizing multi-national data diversity rather than relying on complex algorithmic invariance.

cs.CV↗

arXiv2026

Efficient Multi-scale Masked Autoencoders with Hybrid-Attention Mechanism for Breast Lesion Classification

Self-supervised learning (SSL) with Vision Transformers (ViT) has shown immense potential in medical image analysis. However, the quadratic complexity ($\mathcal{O}(N^2)$) of standard self-attention poses a severe barrier for high-resolution biomedical tasks, effectively excluding resource-constrained research labs from utilizing state-of-the-art models. To address this computational bottleneck without sacrificing diagnostic accuracy, we propose \textbf{MIRAM}, a Multi-scale Masked Autoencoder that leverages a \textbf{hybrid-attention mechanism}. Our architecture uniquely decouples semantic learning from detail reconstruction using a dual-decoder design: a standard transformer decoder captures global semantics at low resolution, while a linear-complexity decoder (comparing Linformer, Performer, and Nyströmformer) handles the computationally expensive high-resolution reconstruction. This reduces the complexity of the upscaling stage from quadratic to linear ($\mathcal{O}(N)$), enabling high-fidelity training on consumer-grade GPUs. We validate our approach on the CBIS-DDSM mammography dataset. Remarkably, our \textbf{Nyströmformer-based variant} achieves a classification accuracy of \textbf{61.0\%}, outperforming both standard MAE (58.9\%) and MoCo-v3 (60.2\%) while requiring significantly less memory. These results demonstrate that hybrid-attention architectures can democratize high-resolution medical AI, making powerful SSL accessible to researchers with limited hardware resources.

cs.CV↗