SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning.

Abstract

Spatial quantification is a critical step in most computational pathology tasks, from guiding pathologists to areas of clinical interest to discovering tissue phenotypes behind novel biomarkers. To circumvent the need for manual annotations, modern computational pathology methods have favored multiple-instance learning approaches that can accurately predict whole-slide image labels, albeit at the expense of losing their spatial awareness. Here we prove mathematically that a model using instance-level aggregation could achieve superior spatial quantification without compromising on whole-slide image prediction performance. We then introduce a superpatch-based measurable multiple-instance learning method, SMMILe, and evaluate it across 6 cancer types, 3 highly diverse classification tasks and 8 datasets involving 3,850 whole-slide images. We benchmark SMMILe against nine existing methods using two different encoders-an ImageNet pretrained and a pathology-specific foundation model-and show that in all cases SMMILe matches or exceeds state-of-the-art whole-slide image classification performance while simultaneously achieving outstanding spatial quantification.