# Polyp Generalization via Diversifying Style at Feature-Level Space
## 1 Introduction  
In the development of effective convolutional neural networks (CNNs) for polyp segmentation, numerous approaches have been proposed and have demonstrated satisfactory performance over time [1; 2; 3]. Traditional deep learning models typically assume that training and testing data are identical and independently distributed. However, in real-world scenarios, especially when deploying segmentation models to predict polyps from entirely new centers, it becomes crucial to accurately segment them regardless of variations in styles, features, shapes, or illumination not present in the training data. Unfortunately, a significant challenge arises due to the domain shift problem, where the distribution of data in the testing environment differs from that of the training data, leading to a drop in the performance of trained CNN models [4; 5]. Style discrepancy is one of the factors that can impede the generalization ability of deep learning models [6]. The discrepancy in style features between datasets exacerbates the domain shift problem, causing issues in real-time applications such as inaccurate polyp diagnosis and analysis, potentially impacting patient screening and treatment plans. Usually, style discrepancy refers to differences in visual characteristics, such as texture, color, or contrast, between datasets used for training and testing CNN models. These style features encompass various aspects of image appearance that may vary significantly across different sources or environments. For instance, variations in lighting conditions, imaging equipment, or image processing techniques can lead to distinct visual styles in the data, as shown in Figure 1. The presence of style discrepanciesbetween training and testing datasets can pose a significant challenge to CNN models, as they may struggle to generalize across these divergent visual styles, ultimately leading to performance degradation when deployed in real-world settings. To address the challenge of domain shift, extensive research has been conducted to develop a generalized model capable of performing effectively in novel environments. Domain adaptation is one approach that aims to align the feature distributions between training source data and target data in a domain-invariant setting [7]. However, this method typically requires access to target domain samples during training, which may not always be feasible in medical contexts. Alternatively, domain generalization (DG) involves incorporating multiple domains from various sources into a target domain without directly using target domain data. DG aims to train a model from one or more related domains to enable direct generalization to any unseen target domain without additional adjustments [8]. The primary objective of DG is to enhance the generalization ability of trained models across diverse domains and facilitate adaptation to new scenarios. Nonetheless, a common assumption in DG is that testing data share the same distribution as the training set, which may not always hold true in real-world medical applications. Among recent advances in domain generalization (DG) models, those based on data manipulation show promising performance [9]. These models utilize data augmentation techniques with a learning-based approach to generate diverse data, complementing the original data and simulating unseen domains to enhance the learning process. Existing DG methods in data augmentation primarily focus on image-level manipulation in the source domain, such as translating images based on styles from auxiliary datasets [10] or converting images from different training domain styles [11]. In computer vision tasks, applying neural style transfer for image-level data augmentation has shown improvement in robustness against domain shift problems [12]. [PERSON] et al. [6] highlighted that convolutional neural networks (CNNs) tend to be biased towards textural features rather than class-specific features, such as shape, leading to challenges in adapting to unseen  
Figure 1: (Best viewed in color).