Scribble-based fast weak-supervision and interactive corrections for
  segmenting whole slide images
Antoine Habis Roy Rosman Nathanson Vannary Meas-Yedid Elsa D. Angelini Jean-Christophe Olivo-Marin
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).