## 1 Introduction
### Use Case 2: Prediction of Pituitary Adenoma Subtypes and Their Neuroendocrine Features  
Better neuroimaging techniques and diagnostic modalities recognize more pituitary adenomas than previously thought [15]. We consider three clinical subclasses: Pituitary adenomas with A. prominent neuroendocrine symptoms, B. slowly developing, insidious, nonspecific complaints delaying accurate diagnosis, or C. incidentally detected adenomas being symptom-free. It remains, therefore, challenging to accurately determine the prevalence and incidence of pituitary adenomas in the general population. They account for 15% of all intracranial neoplasms, being the third most frequent tumor type after meningiomas and gliomas. In multiple postmortem studies, the mean prevalence of pituitary adenomas was 14.4% [15]. The overall estimated prevalence of pituitary adenomas in the general population was calculated as 16.7%. Radiography studies showed a higher prevalence of 22.5% [15; 16]. The tumor has its maximum age frequency in patients between 40 and 60 years of age. The frequency of different subtypes varies depending on the age and gender of the patients [16]. The WHO classification of pituitary adenoma from 2017 is based mainly on the hormone and transcription factor expression of the adenoma cells [17]. In common routine workup for adenomas of the pituitary gland, the morphological evaluation is based, therefore, on H&E and a panel of immunohistochemical staining for all pituitary hormones (adrenoocortic hormone (ACTH), luteinizing hormone (LH), follicle-stimulating hormone (FSH), prolactin (PRL), thyroid-stimulating hormone (TSH), and somatotropic hormone (STH)) and transcription factors. In our study, we focused on corticotroph and gonadotropin adenomas (see Figure 2) since they represent the most common subtypes. We labeled our tumor samples of corticotroph and gonadotroph adenomas accordingly, e.g., corticotroph adenoma, gonadotroph adenoma with the expression of LH, and gonadotroph adenoma with the expression of FSH. As adenomas are often nonexclusively positive for only one hormone, many cases received more than one label. Therefore, we chose to tackle the problem as a multilabel approach, which means that the different classes are rated and scored individually, and possible correlations must be learned by the CNN. To make sure that the labels are correct for each tile, we manually reviewed the extracted regions from the H&E slides with the corresponding regions in the immunohistochemistry stained images. In addition, we included those corticotroph adenomas as a separate class, in which the patient does not show clinical symptoms of Morbus Cushing (silent corticotropic adenoma). The DL tasks were to classify entities of adenomas of the pituitary gland from H&E-stained slides as well as to predict the clinical parameter of asymptomatic or clinically silent corticotroph adenomas. What is new:  
The depicted library enables users to perform DL with state-of-the-art techniques without the burden of managing WSI-associated overhead, such as pyramid level control or region-specific mapping, as it is kept away from the user. Additionally, the library is fully compatible with one of the most popular deep learning frameworks \"fastai\" which is based on \"PyTorch\". Related work:  
In the context of neuropathology-related tasks, few works have been published. Some work has been completed on classifying and detecting Alzheimer's associated lesions, such as extracellular amyloid and intracellular tau deposits [4,18,19]. The latter approach has also been used to classify other tauopathies such as [PERSON]'s disease for example [20]. Additionally, with the help of deep learning new disease-correlating features were identified in the white matter of different tauopathies [21]. Classifying glioma and differentiating glioma subtypes from H&E-stained slides and molecular markers was another successful task accomplished [22].