Digital pathology: Multiple instance learning can detect Barrett´s cancer

We study diagnosis of Barrett´s cancer from hematoxylin & eosin (H & E) stained histopathological biopsy images using multiple instance learning (MIL). We partition tissue cores into rectangular patches, and construct a feature vector consisting of a large set of cell-level and patch-level features for each patch. In MIL terms, we treat each tissue core as a bag (group of instances with a single group-level ground-truth label) and each patch an instance. After a benchmarking study on several MIL approaches, we find that a graph-based MIL algorithm, mi-Graph [1], gives the best performance (87% accuracy, 0.93 AUC), due to its inherent suitability to bags with spatially-correlated instances. In patch-level diagnosis, we reach 82% accuracy and 0.89 AUC using Bayesian logistic regression. We also pursue a study on feature importance, which shows that patch-level color and texture features and cell-level features all have significant contribution to prediction.