Survival prediction using Whole-Slide Images (WSIs) is a cornerstone of precision oncology, yet it remains a formidable challenge due to the massive scale of gigapixel images and the intricate heterogeneity of the tumor microenvironment. While Multiple Instance Learning (MIL) has emerged as a predominant paradigm, existing methods often struggle to balance the capture of fine-grained morphological gradients with the efficient modeling of long-range prognostic dependencies in ultra-long sequences. This study presents MILA-MIL, a novel dual-branch framework designed to bridge the gap between local micro-anatomical nuances and global survival outcomes. To address the inherent directional sensitivity of pathological features, we propose a Pinwheel Convolution (P-Conv) module that introduces a pathology-specific inductive bias for capturing directional morphological gradients. Complementing this, a Mamba-inspired Linear Attention (MILA) branch is developed to facilitate efficient global context modeling with strictly linear complexity, ensuring robust scalability to large-scale clinical datasets. A gated fusion mechanism is further utilized to dynamically integrate these decoupled representations into a discriminative prognostic manifold. Extensive evaluations across six diverse cancer cohorts demonstrate that MILA-MIL consistently achieves state-of-the-art performance, offering superior stability and predictive power over existing MIL aggregators. By synergizing directional morphology with scalable global modeling, this work provides a robust and interpretable solution for computational pathology, with significant potential to enhance clinical decision-making in personalized cancer management.
Li et al. (Thu,) studied this question.
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