This paper proposes a dynamic interactive and weakly supervised segmentation method with minimal user interactions to address two major challenges in the segmentation of whole slide histopathology images. First, the lack of hand-annotated datasets to train algorithms. Second, the lack of interactive paradigms to enable a dialogue between the pathologist and the machine, which can be a major obstacle for use in clinical routine. We therefore propose a fast and user oriented method to bridge this gap by giving the pathologist control over the final result while limiting the number of interactions needed to achieve a good result (over 90\% on all our metrics with only 4 correction scribbles).
@article{arxiv.2402.08333,
title = {Scribble-based fast weak-supervision and interactive corrections for segmenting whole slide images},
author = {Antoine Habis and Roy Rosman Nathanson and Vannary Meas-Yedid and Elsa D. Angelini and Jean-Christophe Olivo-Marin},
journal= {arXiv preprint arXiv:2402.08333},
year = {2024}
}