Multiple-instance-learning-based detection of coeliac disease in histological whole-slide images
Jim Denholm, B.A. Schreiber, Shelley Evans, O.M. Crook, Akshay Sharma, Janella Watson, H. Bancroft, Gerald Langman, Julian Gilbey, C.-B. Schönlieb, Mark J. Arends, Elizabeth J. Soilleux
Abstract
We present a multiple-instance-learning-based scheme for detecting coeliac disease, an autoimmune disorder affecting the intestine, in histological whole-slide images (WSIs) of duodenal biopsies. We train our model to detect two distinct classes, normal tissue and coeliac disease, on the patch-level, and in turn leverage slide-level classifications. Using five-fold cross-validation in a training set of 1841 (1163 normal; 680 coeliac disease) WSIs, our model classifies slides as normal with accuracy (96.7 ± 0.6)%, precision (98.0 ± 1.7)% and recall (96.8 ± 2.5)%, and as coeliac disease with accuracy (96.7 ± 0.5)%, precision (94.9 ± 3.7)% and recall (96.5 ± 2.9)%, where the error bars are the cross-validation standard deviation. We apply our model to two test sets: one containing 191 WSIs (126 normal; 65 coeliac) from the same sources as the training data, and another from a completely independent source, containing 34 WSIs (17 normal; 17 coeliac), obtained with a scanner model not represented in the training data. Using the same-source test data, our model classifies slides as normal with accuracy 96.5%, precision 98.4% and recall 96.1%, and positive for coeliac disease with accuracy 96.5%, precision 93.5% and recall 97.3%. Using the different-source test data the model classifies slides as normal with accuracy 94.1% (32 / 34), precision 89.5% and recall 100%, and as positive for coeliac disease with accuracy 94.1%, precision 100% and recall 88.2%. We discuss generalising our approach to screen for a range of pathologies.