## 4 Discussion
### Unsupervised Segmentation in Pathology and the Problem of Obtaining Meaningful Labels (Ad i)  
Image segmentation is, for many projects in the realm of Digital Pathology or Pathomics, an important early step. There is a legion of different approaches that, based on the training setting, can be broadly categorised as fully supervised, weakly supervised, and unsupervised approaches. For the supervised training approaches, the necessary annotations are time-consuming and tedious to produce. Indeed, the shortage of such annotated datasets is a well-known obstacle [3, 7, 30]. Needing less annotated data for weakly supervised or no annotated data at all for unsupervised methods sounds, in this context, very promising. In addition, by using unsupervised approaches, the need to tailor to every project a well-annotated training set for machine learning models will be reduced to gathering a fitting image collection [10]. Regarding weakly supervised approaches in pathology, generative adversarial networks can be used (after training in a weakly supervised setting) to generate synthetic data based on a small dataset [5, 33]. However, this would mean adding a training cost-intensive step before the actual segmentation model training. Regarding unsupervised image segmentation, in pathology, there are several published approaches [5, 9]. These approaches cover a vast methodological spectrum with, for example, the combination of feature extraction and subsequent clustering [9, 34] or the application of auto-encoders for classification or staining adaption [5, 35, 36, 37]. In a nutshell, there are various working, published, easily adaptable, unsupervised approaches for segmenting (histological) images into different morphological regions. This would then overcome the problem of data scarcity. Unfortunately, this advantage brings a new problem. The labels generated based on morphological aspects (e.g., 'blue granular area') cannot in every case be simply mapped to (histologically) meaningful labels such as 'carcinoma'. One solution to this is to assign names to the labels by human experts. For example, we could allow the expert to define blue areas with many small cells as lymphoid infiltration. However, this expert approach only works if exactly one label is generated per annotation. A multiphase process such as a tumour consisting of tumour cells, stroma, and inflammatory infiltrate, etc., will not be nameable in this manner. For such multiphase entities, the true annotation can be considers as a combination of the morphological labels. However, this linear combination is too simplistic for many areas, as the context is then missing. For example, a homogeneous, blue area can be part of the sky or a blue car. In this regard, there are works that use graphs to include the neighbourhood relationships of the individual labels. For example, [PERSON] et al. used graphs of regions to combine the visual and spatial characteristics of different image parts to meaningful image-part groupings [38]. Alternatively, [PERSON] et al. used local graphs to combine labels in image regions [39]. Our approach, by contrast, is a simple linear combination of the different labels generated by unsupervised learning based on morphological similarity (by a adding a fully connected layer to a CNN model; see B in Figure 3). This linear combination is in analogy to the pathological thinking of tissue or organs as a combination of different structures such as epithelium, stroma, blood vessels, etc. [40, 41]. However, this approach ignores neighbourhood relations or local aspects. CNN Models Previously Trained in an Unsupervised Manner Can Be Adapted to Produce Meaningful Histological Labels (Ad ii)  
As we have discussed, unsupervised training approaches can be used for histological images, but they produce distinct regions or labels based on morphology (e.g., reddish area with little texture) without histologically meaningful labels (such as, e.g., 'fibrosis').