Aim

In digital pathology, we have access to a lot of whole slide images. However, these images often lack annotations and therefore are discarded when training machine learning algorithms. The goal of his Ph.D. is to find a way to revalue these data through self-supervision. Self-supervision is a field of machine learning that aims at learning object representation without any labels. This can be achieved by using various tricks such as in-painting or data augmentation. As a result, we are capable to describe tissues from complex whole-slide images and predicting segmentation maps. The segmentation maps can be used to predict clinically relevant metrics such as tumor border configuration as depicted in the example Fig.1. Tumor border configuration gives an indication of the level of infiltration of the tumor which can be used to predict patient overall and disease-free survival.