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Histo-pathological diagnostics are an inherent part of the everyday work butare particularly laborious and associated with time-consuming manual analysisof image data. In order to cope with the increasing diagnostic case numbers dueto the current growth and demographic change of the global population and theprogress in personalized medicine, pathologists ask for assistance. Profitingfrom digital pathology and the use of artificial intelligence, individualsolutions can be offered (e.g. detect labeled cancer tissue sections). Thetesting of the human epidermal growth factor receptor 2 (HER2) oncogeneamplification status via fluorescence in situ hybridization (FISH) isrecommended for breast and gastric cancer diagnostics and is regularlyperformed at clinics. Here, we develop an interpretable, deep learning(DL)-based pipeline which automates the evaluation of FISH images with respectto HER2 gene amplification testing. It mimics the pathological assessment andrelies on the detection and localization of interphase nuclei based on instancesegmentation networks. Furthermore, it localizes and classifies fluorescencesignals within each nucleus with the help of image classification and objectdetection convolutional neural networks (CNNs). Finally, the pipelineclassifies the whole image regarding its HER2 amplification status. Thevisualization of pixels on which the networks' decision occurs, complements anessential part to enable interpretability by pathologists.

An interpretable automated detection system for FISH-based HER2 oncogene  amplification testing in histo-pathological routine images of breast and  gastric cancer diagnostics