## 4 Discussion
We addressed the classification task on predictions per tile and collected all votes for the given slides of a patient's case. We then obtained the final diagnosis by majority voting to obtain predictions on a case basis. If more than 50% of the tiles of one case were labeled with one class, the case was given that class label. We chose that option for two reasons. First, different from finding metastasis in lymph nodes where high sensitivity is needed, histological slides from pituitary adenomas usually contain massive adenoma; hence, most of the tissue on the slide belongs to the tumor. Second, time was not a major concern. We could simply take and analyze all possible tiles instead of only taking a representative batch for inference. Limitations and Potential Solutions Moving into the Future  
A well-recognized obstacle in digital pathology represents batch effects including variation in staining intensity or fixation artifacts [4; 43]. We contained such batch effects in our input data through hand-picked ROI and normalization. We did not directly address the problem of stain normalization [44]for this dataset, because all staining was performed in a single lab, and only one device was used for scanning. For further usage of our model in a production environment with whole slide images from other institutes, this would be crucial. We are continuously working on this issue to make our models more robust in the future. Histopathology analysis represents a gold standard in tumor diagnosis as it often directs further treatment. Adenomas of the pituitary gland, although routinely classified by immunohistochemical profiling of their neuroendocrine axis, are in urgent need of a clinically meaningful histopathology classification of their risk for relapse. This was partially addressed by the WHO classification from 2004 and 2016. The criteria of atypia to label more aggressive adenomas has been removed, however, as it has not proved a predictive marker [17; 45]. The \"silent\" corticotroph class of our dataset did represent another clinical parameter of interest and was remarkably well recognized by our network, even in the evenly distributed dataset. The good classification result of the \"silent\" corticotroph class in our study shows that neuronal networks are capable of revealing such clinical information hidden within tissue slides and, hence, it may also be possible to extract a clinical relapse parameter from tissue slides via DL. However, due to the lack of datasets stained at different labs, digitized from different scanners, and the size of the dataset, our well-performing models may be unsuitable for clinical practice yet. In conclusion, we developed a convenient open-access library compatible with fastai to support hypothesis-driven DL research projects in the realm of neuropathology. It helps in managing the dataset by assigning hierarchy levels such as patients, cases, and slides, thereby making it easily possible to split the dataset for training and evaluation. The library consists of building blocks fully compatible with fastai for easy integration and usage of the full spectrum of fastai functionality. Additionally, many visualization methods for evaluation are implemented. Both use-cases demonstrated the successful diagnosis of adenoma of the pituitary gland and distinguishing between DNET and GG by H&E-stained slides only and without the necessity of cost- and labor-intense immunohistochemistry staining.