Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection

Nicolas Brieu, Armin Meier, Ansh Kapil, Ralf Schoenmeyer, Christoc G Gavriel, Peter D Caie, Guenter Schmidt

Abstract: The detection of nuclei is one of the most fundamental components of computational pathology. Current state-of-the-art methods are based on deep learning, with the prerequisite that extensive labeled datasets are available. The increasing number of patient cohorts to be analyzed, the diversity of tissue stains and indications, as well as the cost of dataset labeling motivates the development of novel methods to reduce labeling effort across domains. We introduce in this work a weakly supervised 'inter-domain' approach that (i) performs stain normalization and unpaired image-to-image translation to transform labeled images on a source domain to synthetic labeled images on an unlabeled target domain and (ii) uses the resulting synthetic labeled images to train a detection network on the target domain. Extensive experiments show the superiority of the proposed approach against the state-of-the-art 'intra-domain' detection based on fully-supervised learning.

Keywords: Digital Pathology, Cell Detection, Stain Color Normalization, Domain Translation

TL;DR: We present a novel approach for weakly supervised nuclei detection on a target stain domain for which no annotation is available, given only cell center annotations on another source stain domain.