Applicability of the regression approach for histological multi-class grading in clear cell renal cell carcinoma.

ABSTRACT: The histological grading of carcinoma has been one of the central applications of task-specific deep learning in pathology. The deep learning method has pushed away the regression approach, which has been exploited for two-class classification, to address multi-class classification. However, the applicability of the regression approach on multi-class carcinoma grading has not been extensively investigated. Here, we show that the regression approach is sufficiently compatible with classification regarding the four-class grading of clear cell renal cell carcinoma using 11,826 histological image patches from 16 whole slide images. Using convolutional neural network models (DenseNet-121 and Inception-v3), we found that regression models predict as accurately as classification models, achieving

SUBMITTER: Shibata M 

PROVIDER: S-EPMC11802374 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature