Artificial intelligence pathological diagnosis method for renal clear cell carcinoma based on deep learning

Abstract

The invention relates to an artificial intelligence pathological diagnosis method for renal clear cell carcinoma based on deep learning. The method comprises the following steps: S1, data acquisition;S2, pathological microscopic image processing; S3, modular image feature information extraction; S4, machine deep learning and diagnosis model construction; S5, diagnosis efficiency verification of the artificial intelligence diagnosis model: taking the artificial intelligence diagnosis model constructed by the image data in the training set as a diagnosis classifier, inputting the feature information data extracted by the test set for prediction, and evaluating the diagnosis efficiency of the artificial intelligence diagnosis model through a subject working feature curve; and S6, predictionefficiency research of survival prognosis of patients with renal clear cell carcinoma. The invention further provides a renal clear cell carcinoma artificial intelligence pathological diagnosis modelbased on deep learning. The method can effectively predict the survival prognosis of patients with renal clear cell carcinoma, can achieve the effect that cannot be achieved by traditional film reading diagnosis of pathologists, and can provide effective guidance opinions for judging whether the patients with renal clear cell carcinoma continue to be treated or not after operations.

Description

technical field [0001] The present invention relates to the field of medical artificial intelligence, and relates to a deep learning-based artificial intelligence pathological diagnosis method for renal clear cell carcinoma and its diagnostic model Background technique [0002] Renal cell carcinoma is the most common malignant disease of the kidney, accounting for approximately 90% of renal tumors. Its pathological classification includes renal cell carcinoma (Renal cell carcinoma), chromophobe carcinoma, papillary cell carcinoma, collecting duct carcinoma, and unclassified carcinoma. Among them, renal clear cell carcinoma is the most important pathological type, accounting for about 70% to 80% of the total renal cell carcinoma. And other non-clear cell carcinoma poor. Therefore, accurate diagnosis of clear cell renal cell carcinoma has extremely important social significance and value. [0003] Pathological diagnosis is currently the gold standard for the diagnosis of cl...

AI Technical Summary

Problems solved by technology

However, the artificial intelligence pathological diagnosis method of clear cell renal cell carcinoma based on deep learning has not been reported yet.

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