  Histo-pathological diagnostics are an inherent part of the everyday work but
are particularly laborious and associated with time-consuming manual analysis
of image data. In order to cope with the increasing diagnostic case numbers due
to the current growth and demographic change of the global population and the
progress in personalized medicine, pathologists ask for assistance. Profiting
from digital pathology and the use of artificial intelligence, individual
solutions can be offered (e.g. detect labeled cancer tissue sections). The
testing of the human epidermal growth factor receptor 2 (HER2) oncogene
amplification status via fluorescence in situ hybridization (FISH) is
recommended for breast and gastric cancer diagnostics and is regularly
performed at clinics. Here, we develop an interpretable, deep learning
(DL)-based pipeline which automates the evaluation of FISH images with respect
to HER2 gene amplification testing. It mimics the pathological assessment and
relies on the detection and localization of interphase nuclei based on instance
segmentation networks. Furthermore, it localizes and classifies fluorescence
signals within each nucleus with the help of image classification and object
detection convolutional neural networks (CNNs). Finally, the pipeline
classifies the whole image regarding its HER2 amplification status. The
visualization of pixels on which the networks' decision occurs, complements an
essential part to enable interpretability by pathologists.
