###### Abstract  
Background: Segmentation is, in many Pathomics projects, an initial step. Usually, in supervised settings, well-annotated and large datasets are required. Regarding the rarity of such datasets, unsupervised learning concepts appear to be a potential solution. Against this background, we tested for a small dataset on lung cancer tissue microarrays (TMA) if a model (i) first can be in a previously published unsupervised setting and (ii) secondly can be modified and retrained to produce meaningful labels, and (iii) we finally compared this approach to standard segmentation models. Methods: (ad i) First, a convolutional neuronal network (CNN) segmentation model is trained in an unsupervised fashion, as recently described by [PERSON] et al. (ad ii) Second, the model is modified by adding a remapping block and is retrained on an annotated dataset in a supervised setting. (ad iii) Third, the segmentation results are compared to standard segmentation models trained on the same dataset. Results: (ad i-ii) By adding an additional mapping-block layer and by retraining, models previously trained in an unsupervised manner can produce meaningful labels. (ad iii) The segmentation quality is inferior to standard segmentation models trained on the same dataset. Conclusions: Unsupervised training in combination with subsequent supervised training offers for histological images here no benefit.  
Keywords:histopathology; lung cancer; supervised segmentation; unsupervised segmentation +
Footnote †: journal:  
approaches typically comprise four development phases: Phase 1 is the creation of a well-labelled dataset; phase 2 is the choice of the model architecture; phase 3 is the design or choice of an appropriate loss function; and phase 4 is choosing or defining an appropriate optimiser [4]. For phase 1, in a usual supervised setting, to avoid overfitting, typically, large annotated datasets are necessary. Creating a representative, large training database tends to be tedious, especially the segmentation tasks; therefore, good datasets are scarce [3; 5; 6; 7]. To overcome this limitation, several publicly available databases are available online--for example, the Atlas of Digital Pathology [8]. Unfortunately, such databases do not help with more specific questions than segmenting different, non-neoplastic tissues. In addition, rare entities cannot be covered. Many technically different methods have been implemented to overcome the dependency on laboriously generated huge databases. These methods either reduce the number of annotated data needed or are completely independent of labelled data. In addition to approaches based on generated features, of particular interest in this study are machine learning methods that learn the features independently [5; 9; 10]. On one hand, some methods apply machine learning on small datasets, such as few-shot learning [11; 12] or zero-shot learning [13; 14]. On the other hand, there are completely unsupervised learning methods for classification or segmentation tasks [5].  
Against this background, recent publications by [PERSON] et al. on unsupervised image segmentation are of substantial interest. They describe a framework to train CNN segmentation models that is completely unsupervised [15; 16].  
In this work, (i) we tested this approach for the segmentation of non-small cell lung carcinoma in tissue microarrays as an example. Furthermore, (ii) we addressed the problem that unsupervised segmentation leads to undefined labels. To map the labels by the unsupervised training to known, meaningful labels (e.g., adenocarcinoma), we tested a second training step with a small human-labelled dataset.