Novel Conditional Metadata Embedding Data Preprocessing Method for Semantic Segmentation

Semantic segmentation is one of the key research areas in computer vision, which has very important applications in areas such as autonomous driving and medical image diagnosis. In recent years, the technology has advanced rapidly, where current models have been able to achieve high accuracy and efficient speed on some widely used datasets. However, the semantic segmentation task still suffers from the inability to generate accurate boundaries in the case of insufficient feature information. Especially in the field of medical image segmentation, most of the medical image datasets usually have class imbalance issues and there are always variations in factors such as shape and color between different datasets and cell types. Therefore, it is difficult to establish general algorithms across different classes and robust algorithms that differ across different datasets. In this paper, we propose a conditional data preprocessing strategy, i.e., Conditional Metadata Embedding (CME) data preprocessing strategy. The CME data preprocessing method will embed conditional information to the training data, which can assist the model to better overcome the differences in the datasets and extract useful feature information in the images. The experimental results show that the CME data preprocessing method can help different models achieve higher segmentation performance on different datasets, which shows the high practicality and robustness of this method.