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An important task of pathologists is to assign a grade during a tumor diagnosis. The grade correlates with a prognosis, that indicates the chances of survival. This prognosis plays a central role in therapy selection and prospects. Breast carcinomas have many histological subtypes, which are quite similar among themselves. Nevertheless, the pathologists need to perform the grading as accurately as possible. There are multiple grading schemes available. All schemes evaluate the 3 criteria: tubule formation, nuclear pleomorphism and mitotic count. During the evaluation, a label between 1-3 is assigned to each criterion. Afterward, the sum of all labels determines the tumor grade. Whereas the mitotic count is a rather objective evaluation, the tubule formation and nuclear pleomorphism labels are subjective decisions and therefore a strong inter-observer variability exists. Having an algorithm that could provide a deterministic evaluation, would improve reliability and could also improve diagnostic accuracy. The goal of this bachelor thesis is to develop a machine learning-based framework, that on the one hand classifies the grade of the subjective criteria (i.e., tubule formation and nuclear pleomorphism) and on the other hand semantically segments important tissue regions used for the prediction. The overall goal of this framework is to support pathologists in clinical decision-making by providing not only a score for each category per image, but also segmentation information and thus providing a more detailed and interpretable decision support. The dataset provided for this thesis contains mammary tumors in dogs. Due to the similarity between canine and human mammary tissue, successes achieved with canine tissue could be directly transferred to humans. Since annotating many images to train a supervised model is time-consuming, a relatively small dataset is provided. According to that limitation, the first research topic is how to overcome this problem by using transfer learning, image augmentation and image preprocessing. Secondly, it will be evaluated whether the outputs of the segmentation and classification tasks are precise enough to be used by a pathologist.