# Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network

arXiv: https://arxiv.org/abs/2106.10641 · HTML: https://mlsanity.com/paper/2106.10641/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 20 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://mlsanity.com/paper/1904.11492/) (2019)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)
- [CBNet: A Novel Composite Backbone Network Architecture for Object Detection](https://mlsanity.com/paper/1909.03625/) (2019)
- [Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images](https://mlsanity.com/paper/2008.05332/) (2020)
- [Accurate Nuclear Segmentation with Center Vector Encoding](https://mlsanity.com/paper/1907.03951/) (2019)

## Built on by (most cited first, 8 of 8)

- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image](https://mlsanity.com/paper/2106.12265/) (2021)
- [Visual Textualization for Image Prompted Object Detection](https://mlsanity.com/paper/2506.23785/) (2025)
- [Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation](https://mlsanity.com/paper/2306.02691/) (2023)
- [A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation](https://mlsanity.com/paper/2110.13846/) (2021)
- [PIMIP: An Open Source Platform for Pathology Information Management and Integration](https://mlsanity.com/paper/2111.05794/) (2021)
- [Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images](https://mlsanity.com/paper/2504.03146/) (2025)
- [Semantic-Guided Multimodal Preprocessing for Vision Transformer-Based Clear Cell Renal Cell Carcinoma Grading](https://mlsanity.com/paper/2609.01426/) (2026)

## Abstract (arXiv)

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.