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.

The authors have declared no competing interest.

We acknowledge funding and support from Cancer Research UK and the Cancer Research UK Cambridge Centre [CTRQQR-2021-100012], The Mark Foundation for Cancer Research [RG95043], GE HealthCare, and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre [NIHR203312] and EPSRC Tier-2 capital grant [EP/P020259/1]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Ethics Committee of the First Affiliated Hospital of Xian Jiaotong University gave ethical approval (KYLLSL2021-420, KYLLSL2022-333) for this work.

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All data produced in the present study are available upon reasonable request to the authors