Fig. 1: Study Design and DL Model Architecture.

A DACHS cohort and B GALAXY cohort overview including patient characteristics and WSI preprocessing pipeline using UNI, a pretrained vision encoder for feature extraction. C Overview Experimental Setup: Clinical data is fed into DL Model with WSIs for training process and then externally deployed onto the GALAXY cohort to obtain the DL-Score, which are then binarized into DL high-risk and DL low-risk categories. D Architecture of the Transformer-based Multiple Instance Learning (MIL) pipeline. WSIs are divided into patches and preprocessed to feature vectors with a dimension of n-tiles x1024 using the UNI foundation model. Patch feature vectors are then projected to a 512-dimensional vector using a fully connected layer with ReLU activation, with a learnable class token (CLS) added. A two-layer transformer refines the CLS token via self-attention and feedforward networks. The final CLS token, encoding WSI-level information, is processed by an MLP to generate the patient-level risk score. This Figure was partly generated using Flaticon. DACHS=Darmkrebs: Chancen der Verhütung durch Screening Study, WSI=whole-slide image, DFS=disease-free survival, DL=Deep Learning, MRD=molecular residual disease, CLS=class learnable token, MLP=multilayer perceptron.