Accurate spatial quantification in computational pathology with multiple instance learning

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 favoured multiple-instance learning approaches that can accurately predict whole-slide image labels, albeit at the expense of losing their spatial awareness. 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 9 existing methods, 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.