Cell segmentation and classification share a central common problem: the lack of labeled images for training automatic methods. In this regard, adequately addressing the shortcomings of current computational approaches and enabling the clinical use of decision support tools requires training and validation of models on large-scale datasets representative of the wide variability of cases encountered every day in the clinic. At that scale, it is impossible to rely on costly and time-consuming manual annotations. Therefore, to strengthen the application of state-of-the-art segmentation and classification methods in biomedical applications, two strategies are proposed: domain adaptation and weakly supervised machine learning.
Motivated by the promising prospects of deep learning-based segmentation and classification and fueled by the lack of fully annotated training data, in this project, we are interested in studying domain adaptation and weakly supervised methods to train neural networks for cell segmentation and classification. In this sense, we will investigate how to leverage self-training and co-training to train high-quality cell segmentation algorithms using weak labels taking advantage of domain adaptation techniques. Therefore, we will explore weak supervision (extremely one-point annotation per cell), aiming to obtain segmentation performance close to full supervision (mask annotation for each cell) but with the lowest human annotation effort. On the other hand, we will investigate strategies for domain adaptation for cell classification using weak labels. Our focus will be on applications in human sperm cells, cancer biopsies, blood parasites, and neuronal morphologies.