  Segmenting histology images into diagnostically relevant regions is
imperative to support timely and reliable decisions by pathologists. To this
end, computer-aided techniques have been proposed to delineate relevant regions
in scanned histology slides. However, the techniques necessitate task-specific
large datasets of annotated pixels, which is tedious, time-consuming,
expensive, and infeasible to acquire for many histology tasks. Thus,
weakly-supervised semantic segmentation techniques are proposed to utilize weak
supervision that is cheaper and quicker to acquire. In this paper, we propose
SegGini, a weakly supervised segmentation method using graphs, that can utilize
weak multiplex annotations, i.e. inexact and incomplete annotations, to segment
arbitrary and large images, scaling from tissue microarray (TMA) to whole slide
image (WSI). Formally, SegGini constructs a tissue-graph representation for an
input histology image, where the graph nodes depict tissue regions. Then, it
performs weakly-supervised segmentation via node classification by using
inexact image-level labels, incomplete scribbles, or both. We evaluated SegGini
on two public prostate cancer datasets containing TMAs and WSIs. Our method
achieved state-of-the-art segmentation performance on both datasets for various
annotation settings while being comparable to a pathologist baseline.
