## 3 Results  
The main objective of the presented work is to train a machine learning model to find the sparsely, inhomogeneously distributed, prognostically relevant parts of a WSI) of CRC, for a given endpoint, in our case nodal status (nodal negative/pN0 (N\(-\)) and N+). This goal is targeted within the context of a small, but heterogeneous, and therefore authentic data set. In addition, two methodologically different approaches are being tried out to achieve this goal, each of which has advantages and disadvantages:  
Can Supervised Classification Model Training at the Image Tile Level (Referred to as Instance-Level) Effectively Address a Needle-in-Haystack-like Problem?  
Training on the instance level has the advantage of having more than 10,000 image tiles (n = 14,448 for the central tumor parts and n = 55,334 for the border regions) instead of less than 200 cases (compare A in Figure 1). This approach is adopted to mitigate the issue of insufficient data. The advantageous aspect of this training is counterbalanced by the drawback of considerable label noise. Indeed, the sections below show that only a mean of 10% of the tiles per WSI are informative regarding the nodal status (compare B in Figure 1). In essence, this represents a needle-in-the-haystack dilemma, wherein a multitude of hay pieces and a scant number of needles share identical labels. Thus, the labels are quite noisy (compare Figure 2). During training, the models typically learn features only in the early epochs which can be seen in the loss curves. When the losses of the training and validation cohort (in a non-cross-validation setting) diverge, features detection decreases and memorization sets in [44; 45]. To test, if the color normalization inhibits case and, thereby, label recognition based on the staining, we run two experiments: First, we created a training and testing data set by randomly selecting image tiles from the entire data set and, thereby, possibly spreading tiles of one case over the test, training and validation set. Through this approach, the test data set still consists of tiles not utilized to create the other two data sets. However, there are tiles from a case seen during training within the test set. Within this context, the evaluation metrics of the trained models are nearly or rather conspicuously ideal. Kappa values tend to show an almost perfect agreement with values \(\geq\) 0.830 for mapping central tumor to nodal status (Ce2 No) and mapping tumor border to nodal status (Bo2 No). The respective area under the curve (AUC)s are \(\geq\)0.900 also depicting a nearly ideal separation between N\(-\) and N+. In summary, these values are highly suspicious for memorization. Second, we sorted the tiles such that all tiles of one case are either in the training, the validation or the test set. Label allocation was performed identically as before. It could already be seen during training that the model performance stagnates after a few epochs. The accuracy of the training data set and the validation data set diverge from the beginning and remain at about 0.9 and 0.55, respectively (compare the first two rows for the standard approach in Table 2). This huge gap between the loss values with a considerably elevated validation loss is a common sign of over-fitting. The validation accuracy initially stays in the range \([0.50;\ 0.55]\) throughout the entire training, independent of the number of epochs or the above-mentioned early stopping strategy. In summary, the clinical feature nodal status could not be identified correctly at an AUC of 0.642, which is close to a prediction by chance (compare AUC values in Table 2). Furthermore, these results show that color normalization does not prevent recognizing cases by a CNN model, presumably based on staining characteristics.