Pathological sections are an important basis for pathologists to diagnose and analyze the course and prognosis of tumor patients.However,due to the low degree of automation in the preparation process of slides,both human operation and equipment noise will reduce the quality of the slides,thereby affecting diagnosis.Currently,the quality control of pathological slides mainly uses manual sampling inspection,which has the characteristics of high work intensity and long working hours,and can easily lead to evaluation bias due to visual fatigue.Quality control of pathological slides using deep learning technology attracts attention from the medical and engineering communities and makes certain progress.This paper reviews the research status in this field.First,the production and digitization process of pathological slides is briefly introduced,and the difficulties and challenges in qua-lity control work are analyzed.Then,the existing work related to the quality control of pathological slides is analyzed and summarized,and the method theory and application status of the existing work are reviewed from aspects such as staining standardization,focus quality assessment,artifact detection,image repair and reconstruction.Finally,the possible future research hotspots in this field are prospected.