## 5 Conclusions, Limitations and Outlook  
The work presented has two major findings: First, we can demonstrate the feasibility of combining MIL with data augmentation techniques tailored for tabular data. This integration allows for the application of machine learning to case-based annotations, exemplified in our study by the nodal status in CRC, even when working with small datasets, such as the 117 cases analyzed in this study. Second, the MIL approach, operating unsupervised at the tile level, validates the significance of tumor budding as a diagnostic morphological pattern. This confirmation is derived from observing a disproportionate presence of tumor budding in diagnostically relevant areas, aligning with existing literature on the subject. At least for the small data set here, we can show that the MIL-based approach produces similar results as the human-evaluation-based tumor budding score. Concerning limitations, the primary constraint is the small data set sourced from a single center. While we employed color normalization to address domain issues, such as staining variations, validating the tool on external datasets is imperative. Of course, such validation is not sufficient for routine use. In addition to various technical aspects (e.g., setting up an API), extensive clinical validation would also be necessary. Concerning outlook, in the future, MIL-based approaches can be used in various settings. For instance, tumor budding is not only a known prognostic factor in CRC but also in squamous cell carcinoma of the head and neck region [77]. So, future studies could easily apply the herein presented approach to tumor budding questions in other organ systems. Furthermore, as the herein-described adaptions for small data sets are not limited to tumor budding at all, the approach could be used for small data sets of, for example, uncommon tumors. For example, to stay in the head and neck region, such approaches could be used for differentiating rare spindle cell lesions of the oral cavity, where at the end, undifferentiated sarcoma types need to be separated from spindle cell carcinoma [78; 79; 80; 81]. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14020949/s1](https://www.mdpi.com/article/10.3390/app14020949/s1)]([https://www.mdpi.com/article/10.3390/app14020949/s1](https://www.mdpi.com/article/10.3390/app14020949/s1)), Figure S1: Overview of the complete workflow; Figure S2: Workflow for the segmentation of a HE-stained histological images; Table S1: Palette for segmentation labels; Figure S3: Workflow for the classification of HE-images; Figure S4: Data set preparation for the classification of HE-images. Conceptualization, C.-A.W. and T.G. ; methodology, D.R. and C.-A.W. ; software, D.R., M.R. and C.-A.W. ; validation, D.R. and C.-A.W. ; formal analysis, D.R., C.-A.W. and S.H. ; investigation, D.R. and C.-A.W. ; resources, C.L. ; data curation, C.L. and D.R. ; writing--original draft preparation, D.R., N.E. and C.-A.W. ; writing--review and editing, D.R., C.-A.W., C.L., N.E. and T.G. ; visualization, D.R., N.E. and C.-A.W. ; supervision, T.G. and C.-A.W. ; project administration, C.-A.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted in compliance with the Declaration of Helsinki and approved by the local ethics committee in Graz (decision 18-199 ex 06/07). We obtained an ethics approval (decision 18-199 ex 06/07) from Graz, where patient consent was not deemed necessary due to the study's nature--tissue-based, retrospective, and entirely anonymous. Additionally, we secured an extra ethics approval from Mannheim (decision 2017-806R-MA), which also does not mandate patient consent.