Robust identification of islets with variable morphology in H&E-stained pancreatic tissue using HALO AITM

In this application note, Indica Labs trains the HALO AI^TM VGG convolutional neural network (VGG-CNN) to identify islets within pancreatic tissue sections following H&E staining. It demonstrates how it is possible to build a robust classifier to accurately segment islets from surrounding exocrine tissue, irrespective of stain or morphological variability. This study highlights the potential for HALO AI to simplify the pathological evaluation of pancreatic tissue in metabolic research and toxicological pathology.