Histological data often comes with various imperfections such as noise, inconsistencies, and variations in staining intensity. Preprocessing helps in mitigating these issues, thereby enhancing the quality and reliability of the data. This step is essential to ensure that the subsequent analysis, whether it be morphological assessment, quantitative analysis, or machine learning applications, yields valid and reproducible results.
