Rethinking Attention-Based Multiple Instance Learning for Whole-Slide
  Pathological Image Classification: An Instance Attribute Viewpoint
Linghan Cai Shenjin Huang Ye Zhang Jinpeng Lu Yongbing Zhang
Multiple instance learning (MIL) is a robust paradigm for whole-slide
pathological image (WSI) analysis, processing gigapixel-resolution images with
slide-level labels. As pioneering efforts, attention-based MIL (ABMIL) and its
variants are increasingly becoming popular due to the characteristics of
simultaneously handling clinical diagnosis and tumor localization. However, the
attention mechanism exhibits limitations in discriminating between instances,
which often misclassifies tissues and potentially impairs MIL performance. This
paper proposes an Attribute-Driven MIL (AttriMIL) framework to address these
issues. Concretely, we dissect the calculation process of ABMIL and present an
attribute scoring mechanism that measures the contribution of each instance to
bag prediction effectively, quantifying instance attributes. Based on attribute
quantification, we develop a spatial attribute constraint and an attribute
ranking constraint to model instance correlations within and across slides,
respectively. These constraints encourage the network to capture the spatial
correlation and semantic similarity of instances, improving the ability of
AttriMIL to distinguish tissue types and identify challenging instances.
Additionally, AttriMIL employs a histopathology adaptive backbone that
maximizes the pre-trained model's feature extraction capability for collecting
pathological features. Extensive experiments on three public benchmarks
demonstrate that our AttriMIL outperforms existing state-of-the-art frameworks
across multiple evaluation metrics. The implementation code is available at
https://github.com/MedCAI/AttriMIL.