Centrala begrepp
Enhancing AI models for renal cell carcinoma through meticulous annotation and model development.
Sammanfattning
The article discusses the creation of the RCdpia dataset, focusing on renal cell carcinoma digital pathology image annotation. Two pathologists curated a dataset from TCGA to enhance AI model accuracy. The Resnet model validated the annotated dataset against another hospital's data. The RCdpia dataset includes various kidney cancer cases and is publicly accessible. Model analysis revealed discrepancies in predictive outcomes across different datasets, emphasizing the need for precise AI models in digital pathology.
Statistik
The RCdpia dataset includes 109 cases of kidney chromophobe cell carcinoma, 486 cases of kidney clear cell carcinoma, and 292 cases of kidney papillary cell carcinoma.
The ResNet models achieved an approximate accuracy of 99% across the three subtypes.
The training sets consisted of 9, 12, and 20 WSIs for each subtype from FAHZU respectively.
Citat
"We identified significant disparities in image quality during the annotation process."
"Model accuracy varied significantly when applied to pathological images from different centers."
"Further work is required to normalize WSI data for enhanced model robustness."