Scribble-based fast weak-supervision and interactive corrections for segmenting whole slide images
This addresses the problem of enabling efficient and accurate segmentation for pathologists in clinical settings, though it appears incremental as it builds on existing weak-supervision and interactive paradigms.
The paper tackles the lack of hand-annotated datasets and interactive tools for segmenting whole slide histopathology images by proposing a fast, user-oriented method that achieves over 90% on all metrics with only 4 correction scribbles.
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).