Name
Title | ||
|---|---|---|
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network |
Abstract | ||
|---|---|---|
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 nuclei classification into two cross-category classification tasks that are leaned by two newly designed high-resolution feature extractors (HRFEs). 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 to 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87237-3_13 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VIII |
Keywords | DocType | Volume |
Nuclei grading, Nuclei segmentation, Histopathology | Conference | 12908 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeyu Gao | 1 | 1 | 0.71 |
| Jiangbo Shi | 2 | 0 | 1.01 |
| Xianli Zhang | 3 | 9 | 4.61 |
| Yang Li | 4 | 2 | 2.43 |
| Haichuan Zhang | 5 | 0 | 2.70 |
| Jialun Wu | 6 | 1 | 3.08 |
| Chunbao Wang | 7 | 1 | 3.75 |
| Deyu Meng | 8 | 2025 | 105.31 |
| Chen Li | 9 | 7 | 7.15 |