  The vast majority of semantic segmentation approaches rely on pixel-level
annotations that are tedious and time consuming to obtain and suffer from
significant inter and intra-expert variability. To address these issues, recent
approaches have leveraged categorical annotations at the slide-level, that in
general suffer from robustness and generalization. In this paper, we propose a
novel weakly supervised multi-instance learning approach that deciphers
quantitative slide-level annotations which are fast to obtain and regularly
present in clinical routine. The extreme potentials of the proposed approach
are demonstrated for tumor segmentation of solid cancer subtypes. The proposed
approach achieves superior performance in out-of-distribution, out-of-location,
and out-of-domain testing sets.
