Research progress in pathological diagnosis of bladder cancer based on deep learning
HUANG Zhiqiang
ZHONG Shijiang
WANG Xingping
Abstract：Bladder cancer is a malignant tumor of urinary system with high incidence rate.Pathological diagnosis is the gold standard for confirming bladder cancer.Traditional pathological diagnosis is influenced by differences in the professional level of physicians,and has subjectivity and diagnostic limitations,which may lead to missed diagnosis or misdiagnosis.As an important branch of artificial intelligence,deep learning can improve the repeatability,objectivity and automation of pathological diagnosis of bladder cancer to a certain extent.This not only helps alleviate the pressure of the shortage of pathologists in China,but also promotes the development of precision medicine.At present,the application of deep learning in pathological diagnosis of bladder cancer is still in its initial stage,and this field is faced with many challenges,such as big data and standardization requirements,low model acceptability,ethical and privacy issues,and single data mode.In response to the above challenges,technical breakthroughs such as establishing a standardized database,improving model transparency,and developing multimodal prediction models will effectively promote the implementation and application of deep learning in the practice of pathological diagnosis of bladder cancer.
Keywords：Bladder cancerDeep learningArtificial intelligenceDigital pathologyPathological diagnosis
Publication Date：2025-12-20
Online Publishing Date：2026-01-13(First online date of this platform, not the publication date of the document)
Pages：7( 618-624 )