Application of quantitative histomorphometric features in computational pathology

Abstract

Computer vision has facilitated the execution of various computer‐aided diagnostic tasks. From a methodological perspective, these tasks are primarily implemented using two dominant strategies: end‐to‐end Deep learning (DL)‐based methods and traditional feature engineering‐based methods. DL methods are capable of automatically extracting, analyzing, and filtering features, leading to final decision‐making from whole slide images. However, these methods are often criticized for the “black box” issue, a significant limitation of DL. In contrast, traditional feature engineering‐based methods involve well‐defined quantitative input features. But it was considered as less potent than DL methods. Advances in segmentation technology and the development of quantitative histomorphometric (QH) feature representation have propelled the evolution of feature engineering‐based methods. This review contrasts the performance differences between the two methods and traces the development of QH feature representation. The conclusion is that, with the ongoing progress in QH feature representation and segmentation technology, methods based on QH features will leverage their advantages—such as explainability, reduced reliance on large training datasets, and lower computational resource requirements—to play a more significant role in some clinical tasks. They may even replace DL methods somewhat or be used in conjunction with them to achieve accurate and understandable results.