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Advances in Computational Nephropathology
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DOI：10.1016/j.kint.2025.06.029.png)
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Abstract
En 中文
Pathology relies on pathologists’ qualitative assessment and semi-quantitative measures to characterize the structural and molecular alterations of tissues. Novel analytical methods and recent advances in the computational field, particularly in artificial intelligence (AI) and deep learning in pathology, termed computational pathology, have led to widespread applications and advancement in research. Integrating computational approaches into the digital pathology workflow can facilitate the automated, high-throughput analysis of histopathological images thereby improving precision, reproducibility, and efficiency in pathology diagnostics.
Keywords:
Deep learning
Digital Pathology
Kidney Biopsy
Pathomics
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