  Nuclei instance segmentation on histopathology images is of great clinical
value for disease analysis. Generally, fully-supervised algorithms for this
task require pixel-wise manual annotations, which is especially time-consuming
and laborious for the high nuclei density. To alleviate the annotation burden,
we seek to solve the problem through image-level weakly supervised learning,
which is underexplored for nuclei instance segmentation. Compared with most
existing methods using other weak annotations (scribble, point, etc.) for
nuclei instance segmentation, our method is more labor-saving. The obstacle to
using image-level annotations in nuclei instance segmentation is the lack of
adequate location information, leading to severe nuclei omission or overlaps.
In this paper, we propose a novel image-level weakly supervised method, called
cyclic learning, to solve this problem. Cyclic learning comprises a front-end
classification task and a back-end semi-supervised instance segmentation task
to benefit from multi-task learning (MTL). We utilize a deep learning
classifier with interpretability as the front-end to convert image-level labels
to sets of high-confidence pseudo masks and establish a semi-supervised
architecture as the back-end to conduct nuclei instance segmentation under the
supervision of these pseudo masks. Most importantly, cyclic learning is
designed to circularly share knowledge between the front-end classifier and the
back-end semi-supervised part, which allows the whole system to fully extract
the underlying information from image-level labels and converge to a better
optimum. Experiments on three datasets demonstrate the good generality of our
method, which outperforms other image-level weakly supervised methods for
nuclei instance segmentation, and achieves comparable performance to
fully-supervised methods.
