  The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic
factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis.
Computer-aided nuclei grading aims to improve pathologists' work efficiency
while reducing their misdiagnosis rate by automatically identifying the grades
of tumor nuclei within histopathological images. Such a task requires precisely
segment and accurately classify the nuclei. However, most of the existing
nuclei segmentation and classification methods can not handle the inter-class
similarity property of nuclei grading, thus can not be directly applied to the
ccRCC grading task. In this paper, we propose a Composite High-Resolution
Network for ccRCC nuclei grading. Specifically, we propose a segmentation
network called W-Net that can separate the clustered nuclei. Then, we recast
the fine-grained classification of nuclei to two cross-category classification
tasks, based on two high-resolution feature extractors (HRFEs) which are
proposed for learning these two tasks. The two HRFEs share the same backbone
encoder with W-Net by a composite connection so that meaningful features for
the segmentation task can be inherited for the classification task. Last, a
head-fusion block is applied to generate the predicted label of each nucleus.
Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000
image patches with 70945 annotated nuclei. We demonstrate that our proposed
method achieves state-of-the-art performance compared to existing methods on
this large ccRCC grading dataset.
