In computational pathology, deep learning techniques have outperformed classical algorithms and hand-engineered features by leveraging large amounts of costly annotated data to train models that automatically learn relevant features for the tasks. Barriers to the successful application and design of deep learning approaches in computational pathology include: 1) The need for large image datasets with costly annotations to train deep learning models. 2) Visual variability of the images, which degrade the performance of models trained with homogeneous datasets. Finally, deep learning models in computational pathology usually leave out semantic information from pathology reports and other modalities that are complementary sources of information. This thesis contributes to solving these barriers by setting the following objectives: 1) To reduce the need for expensive pathologists’ annotations. 2) To evaluate and propose methods to overcome the visual heterogeneity of the images. 3) To exploit the semantic information from scientific literature and pathology reports.