Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcome in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. Automation of this process will expectedly increase efficiency and reproducibility. Here we present a deep learning convolutional neural network model that automates the above procedure. For model training we used a semi-supervised learning method, to maximize detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human and machine selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on https://grand-challenge.org/algorithms/colon-budding-in-ihc/.