## 3 Results
### Are the Labels Learned in an Unsupervised Fashion Already Meaningful?  
As we have described, the models trained without supervision produce a predefined number of labels that are not directly connected to labels defined by humans. These labels are based on texture or morphological similarity. To test whether these labels alone correlate to the known structures (e.g., tumour glands) or more to the diagnoses of non-tumourous tissue (NT), adenocarcinoma (ADC), or squamous cell carcinoma (SqCC), in this context, the frequency of these labels per diagnosis was examined.  
The basic idea was that each TMA core has its own label composition or frequency that correlates with the diagnosis. For example, a TMA core from a case with ADC should contain (only) the labels NT and/or ADC. To test this assumption in principle, for human-generated labels, we plotted the label composition (frequency of labels per TMA core) against the known diagnosis per TMA kernel (see A1-2 in Figure 5). Plotting the label frequency per diagnosis (A1 in Figure 5) or running a principal component analysis (PCA) to compare the frequency vectors per TMA core (A2 in Figure 5) verifies this assumption. As expected, cases with, for example, a diagnosis of ADC differ in that only in these cases does the label ADC occur alongside the image background (BG) and non-tumourous tissue (NT).  
Figure 5: **Segmentation results for different models trained.** [PERSON] et al. [15] described a method for training CNN models in an unsupervised fashion, resulting in some labels that are not assigned to certain structures by a human. For testing whether these labels or a combination are already meaningful, the label frequency per TMA core was analysed with respect to the known diagnosis per core (normal tissue (NT), adenocarcinoma (ADC), and squamous cell carcinoma (SqCC)). (**A1,A2**) For testing whether the label frequency per TMA core could correlate with the diagnosis, the label frequency of the manually annotated TMA cores was examined. (**A1**) shows the label frequency (background (BG), normal tissue (NT), adenocarcinoma (ADC), and squamous cell carcinoma (SqCC)) plotted against the diagnosis. (**A2**) shows a PCA (two components) for the label frequency vector per TMA core. (**B1,B2**) For the labels produced by a simple CNN model (previously called concise_CNN) trained in an unsupervised manner, the label frequency was also analysed in regard to the known diagnosis per TMA core. (**B1**) shows the frequency distribution per label and diagnosis. (**B2**) shows a PCA (two components) for the label frequency vectors per TMA core.  
In the next step, we examined the label frequency for the labels generated by the model after unsupervised training. Here, it can be seen that neither plotting the label frequency per diagnosis (B1 in Figure 5)) nor a PCA analysis (B2 in Figure 5) show a reliable correlation between label composition and diagnosis. A distinction between ADC and SqCC is not possible on this basis. Based on these plots, only TMA cores with and without tumour infiltration can be distinguished. A comparable analysis for the standard UNet [20] trained in a supervised setting also showed no sharp separation of diagnosis based on label frequency (compare Appendix A Figure A3). This fits well with the overall moderate segmentation quality of the models trained and validated. In the unsupervised trained or retrained models, the small network size might be causative. In the standard UNet, the training database may not be sufficiently large.