Using machine learning approaches to automate the diagnosis of small intestinal biopsies

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• Professor Elizabeth Soilleux, MA, MB BChir, PhD, FRCPath, PGDipMedEd, SFHEA Professor of Diagnostics and Biomarkers/ Honorary Consultant in Pathology, University of Cambridge
• Monday 24 November 2025, 12:00-12:30
• SS03 Seminar Room, Willam Gates building (Department of Computer Science and Technology).

If you have a question about this talk, please contact Sam Nallaperuma-Herzberg.

Histopathology is a clinical discipline in which pathologists look down microscopes at biopsies, to make a diagnosis. Nowadays many departments scan the glass microscope slides, so that pathologists can view them on a screen. This opens up the possibility of automating histopathological diagnosis, particularly as there are international shortages of pathologists, leading to backlogs and delays. We chose the duodenum (small intestine) as a starting point due to its low medicolegal risk (

We apply a series of processing steps, including artefact removal, division into small tiles and colour normalisation, before applying a multiple instance learning approach to biopsy classification, leading to accuracy >97% against a carefully curated ground truth. In order to facilitate adoption of this technology by pathologists and their clinical colleagues, we are now working on making our categorisation processes more explainable. We have spun out a company, Lyzeum Ltd, to progress our software to market.

This talk is part of the Accelerate Lunchtime Seminar Series series.

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