Impact of a deep learning assistant on the histopathologic classification of liver cancer - npj Digital Medicine
www.nature.com
we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 patho
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Researchers have developed a deep learning-based assistant to help pathologists distinguish between two types of liver cancer. The assistant uses whole-slide images stained with hematoxylin and eosin to differentiate between hepatocellular carcinoma and cholangiocarcinoma. The study evaluated the impact of the assistant on the diagnostic performance of 11 pathologists with different levels of expertise. The results showed that the deep learning assistant significantly improved the accuracy of the pathologists' diagnoses, particularly for less experienced pathologists. This technology has the potential to enhance the histopathologic classification of liver cancer and improve patient outcomes.
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- we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise.
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