  The segmentation and automatic identification of histological regions of
diagnostic interest offer a valuable aid to pathologists. However, segmentation
methods are hampered by the difficulty of obtaining pixel-level annotations,
which are tedious and expensive to obtain for Whole-Slide images (WSI). To
remedy this, weakly supervised methods have been developed to exploit the
annotations directly available at the image level. However, to our knowledge,
none of these techniques is adapted to deal with WSIs. In this paper, we
propose WholeSIGHT, a weakly-supervised method, to simultaneously segment and
classify WSIs of arbitrary shapes and sizes. Formally, WholeSIGHT first
constructs a tissue-graph representation of the WSI, where the nodes and edges
depict tissue regions and their interactions, respectively. During training, a
graph classification head classifies the WSI and produces node-level pseudo
labels via post-hoc feature attribution. These pseudo labels are then used to
train a node classification head for WSI segmentation. During testing, both
heads simultaneously render class prediction and segmentation for an input WSI.
We evaluated WholeSIGHT on three public prostate cancer WSI datasets. Our
method achieved state-of-the-art weakly-supervised segmentation performance on
all datasets while resulting in better or comparable classification with
respect to state-of-the-art weakly-supervised WSI classification methods.
Additionally, we quantify the generalization capability of our method in terms
of segmentation and classification performance, uncertainty estimation, and
model calibration.
