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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.11211 |
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| _version_ | 1866910370569912320 |
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| author | Sun, Qingrong Zhong, Weixiang Zhou, Jie Lai, Chong Teng, Xiaodong Lai, Maode |
| author_facet | Sun, Qingrong Zhong, Weixiang Zhou, Jie Lai, Chong Teng, Xiaodong Lai, Maode |
| contents | The annotation of digital pathological slide data for renal cell carcinoma is of paramount importance for correct diagnosis of artificial intelligence models due to the heterogeneous nature of the tumor. This process not only facilitates a deeper understanding of renal cell cancer heterogeneity but also aims to minimize noise in the data for more accurate studies. To enhance the applicability of the data, two pathologists were enlisted to meticulously curate, screen, and label a kidney cancer pathology image dataset from The Cancer Genome Atlas Program (TCGA) database. Subsequently, a Resnet model was developed to validate the annotated dataset against an additional dataset from the First Affiliated Hospital of Zhejiang University. Based on these results, we have meticulously compiled the TCGA digital pathological dataset with independent labeling of tumor regions and adjacent areas (RCdpia), which includes 109 cases of kidney chromophobe cell carcinoma, 486 cases of kidney clear cell carcinoma, and 292 cases of kidney papillary cell carcinoma. This dataset is now publicly accessible at http://39.171.241.18:8888/RCdpia/. Furthermore, model analysis has revealed significant discrepancies in predictive outcomes when applying the same model to datasets from different centers. Leveraging the RCdpia, we can now develop more precise digital pathology artificial intelligence models for tasks such as normalization, classification, and segmentation. These advancements underscore the potential for more nuanced and accurate AI applications in the field of digital pathology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11211 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RCdpia: A Renal Carcinoma Digital Pathology Image Annotation dataset based on pathologists Sun, Qingrong Zhong, Weixiang Zhou, Jie Lai, Chong Teng, Xiaodong Lai, Maode Computer Vision and Pattern Recognition The annotation of digital pathological slide data for renal cell carcinoma is of paramount importance for correct diagnosis of artificial intelligence models due to the heterogeneous nature of the tumor. This process not only facilitates a deeper understanding of renal cell cancer heterogeneity but also aims to minimize noise in the data for more accurate studies. To enhance the applicability of the data, two pathologists were enlisted to meticulously curate, screen, and label a kidney cancer pathology image dataset from The Cancer Genome Atlas Program (TCGA) database. Subsequently, a Resnet model was developed to validate the annotated dataset against an additional dataset from the First Affiliated Hospital of Zhejiang University. Based on these results, we have meticulously compiled the TCGA digital pathological dataset with independent labeling of tumor regions and adjacent areas (RCdpia), which includes 109 cases of kidney chromophobe cell carcinoma, 486 cases of kidney clear cell carcinoma, and 292 cases of kidney papillary cell carcinoma. This dataset is now publicly accessible at http://39.171.241.18:8888/RCdpia/. Furthermore, model analysis has revealed significant discrepancies in predictive outcomes when applying the same model to datasets from different centers. Leveraging the RCdpia, we can now develop more precise digital pathology artificial intelligence models for tasks such as normalization, classification, and segmentation. These advancements underscore the potential for more nuanced and accurate AI applications in the field of digital pathology. |
| title | RCdpia: A Renal Carcinoma Digital Pathology Image Annotation dataset based on pathologists |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.11211 |