  Ki67 is a significant biomarker in the diagnosis and prognosis of cancer,
whose index can be evaluated by quantifying its expression in Ki67
immunohistochemistry (IHC) stained images. However, quantitative analysis on
multi-source Ki67 images is yet a challenging task in practice due to
cross-domain distribution differences, which result from imaging variation,
staining styles, and lesion types. Many recent studies have made some efforts
on domain generalization (DG), whereas there are still some noteworthy
limitations. Specifically in the case of Ki67 images, learning invariant
representation is at the mercy of the insufficient number of domains and the
cell categories mismatching in different domains. In this paper, we propose a
novel method to improve DG by searching the domain-agnostic subnetwork in a
domain merging scenario. Partial model parameters are iteratively pruned
according to the domain gap, which is caused by the data converting from a
single domain into merged domains during training. In addition, the model is
optimized by fine-tuning on merged domains to eliminate the interference of
class mismatching among various domains. Furthermore, an appropriate
implementation is attained by applying the pruning method to different parts of
the framework. Compared with known DG methods, our method yields excellent
performance in multiclass nucleus recognition of Ki67 IHC images, especially in
the lost category cases. Moreover, our competitive results are also evaluated
on the public dataset over the state-of-the-art DG methods.
