## 3 Results
### Does the Model Architecture Trained in an Unsupervised Manner Influence the Segmentation Quality?  
[PERSON] et al. (2015) demonstrated that with their approach, different CNN models can be trained. As we have described, complex models such as the UNet variant by [PERSON] et al. (2018) do not converge. However, simple model variants for FCN and UNet can be trained and do converge. To compare three different architectures, we first used the unsupervised Kanezaki approach to train the aforementioned models, which consist of a linear combination of convolution blocks (called concise_CNN; sketched in Appendix A Figure A1A), a relatively simple FCN variant (called concise_FCN; Appendix A Figure A1B), and a relatively simple UNet variant (called concise_UNet; Appendix A Figure A1C).  
Figure 4: **Effect of the number of images used for training.** Three models were trained on eight different datasets. The three models are: a small CNN variant as described by [PERSON] et al. (2015) (called concise_CNN), trained in an unsupervised approach and then modified and retrained as described in this work to produce histologically meaningful labels; two UNet models ([PERSON] et al., 2018) with a ResNet backbone pretrained on a tissue classification task (called UNet_histo) and with a ResNet backbone without pretraining (called UNet_histo). The eight datasets are a manual selection (with nine images, three per diagnosis; compare Figure 1) from dataset #1 (called selection), and seven increasingly larger fractions from dataset #2 (called 0.01, 0.05, 0.1, 0.25, 0.5, 0.75, and 1.0) Subsequently, the different models were tested using dataset #2. As a segmentation metric, the F1 score is plotted.  
Next, these three models were retrained in a supervised setting (as described in Section 4.1). To test if there is an advantage of such pretrained models for smaller datasets, we performed retraining by using two training datasets: (i) a manual selection of nine images (three per NT, ADC, and SqCC; called selection) and (ii) the entire dataset #1.  
Regarding segmentation quality measurement, these models were again tested on dataset #2 (the testing dataset).  
For very small retraining datasets (selection; n = 9 images), the Concise_CNN shows better results if only the last layers are retrained (accuracy 0.53 \(\pm\) 0.21 and F1 score 0.34 \(\pm\) 0.11). For the more complex concise_FCN (accuracy 0.50 \(\pm\) 0.24 and F1 score 0.30 \(\pm\) 0.10) and concise_UNet (accuracy 0.43 \(\pm\) 0.11 and F1 score 0.24 \(\pm\) 0.09), the models only show moderate segmentation quality, if the entire models are retrained (Figure A2A).  
For larger retraining datasets (dataset #1; n = 247 images (with 0.8 for training and 0.2 for validation)), there was no significant difference for all three models (Figure A2B). The best model was the simple_FCN. The accuracy was 0.63 \(\pm\) 0.21 and the F1 score was 0.38 \(\pm\) 0.10 when only the last layers were retrained, and the accuracy was 0.61 \(\pm\) 0.15 and the F1 score was 0.387 \(\pm\) 0.07 when all layers were retrained. The worst model was the simple_CNN. For retraining only the last layers, the accuracy was 0.57 \(\pm\) 0.17 and the F1 score was 0.36 \(\pm\) 0.10. For retraining all layers, the accuracy was 0.57 \(\pm\) 0.19 and the F1 score was 0.37 \(\pm\) 0.10.  
Notably, segmentation results were more than 0.1 worse than the results for the complex UNet model variants, such as the Standard_UNET (compare Table 3) by [PERSON] et al. [20].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **Loss Function** & \multicolumn{2}{c}{**Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\
**Size** & **Loss Function** & \multicolumn{2}{c}{**(Accuracy/F1)**} & \multicolumn{2}{c}{**(Accuracy/F1)**} \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **FCN** \\