Résumé : This dissertation studies how the reality of digital pathology annotations affects modern image analysis algorithms, as well as the evaluation processes that we use to determine which algorithms are better. In the ideal supervised learning scenario, we have access to a “ground truth”: the output that we want from the algorithms. This “ground truth” is assumed to be unique, and the evaluation of algorithms is typically based on comparing it to the actual output of the algorithm. In the world of biomedical imaging, and more specifically in digital pathology, the reality is very different from this ideal scenario. Image analysis tasks in digital pathology are trying to replicate assessments made by highly trained experts, and these assessments can be complex and difficult, and therefore come with different levels of subjectivity. As a result, the annotations provided by these experts (and typically considered as “ground truth” in the training and evaluation of deep learning algorithms) are necessarily associated with some uncertainty. Our main contributions to the state-of-the-art are the following. First, we studied the effects of imperfect annotations on deep learning algorithms and proposed adapted learning strategies tocounteract adverse effects. Second, we analysed the behaviour of evaluation metrics and proposed guidelines to improve the choices made in the evaluation processes. Third, we demonstrated how the integration of interobserver variability into the evaluation process can provide better insights into the results of image analysis algorithms, and better leverage the annotations from multiple experts. Finally, we reviewed digital pathology challenges and found important shortcomings in their design choices and in their quality control and demonstrated the need for increased transparency.