MILFORMER: Weighted Dual Stream Class Centered Random Attention Multiple Instance Learning for Whole Slide Image Classification

Whole Slide Imaging (WSI) in contemporary pathology is pivotal, particularly in cancer diagnosis and prognosis. Multiple Instance Learning (MIL) has emerged as a pivotal strategy to address the scarcity of localized annotations in WSI analysis. However, in the current landscape of state-of-the-art methods, the instance-level accuracy of these models significantly lags behind that of the bag-level. This article introduces MILFormer, a novel multi-head class-centered random global attention, enhancing the weighted averaged dual-stream architecture. Our method surpasses existing bag and WSI classification approaches while notably improving instance-level accuracy by up to 8%, marking a substantial advancement in Regions of Interest (ROI) detection. We comprehensively evaluated our method, spanning various datasets, including the public Camelyon16 dataset, and showed that our proposed architecture is effective and robust across a broad spectrum of MIL challenges. Our code is available at: https://github.com/rohban-lab/MILFormer