TexSem-Net: Texture Augmentation and Regional Semantics Fusion Dense-Prediction Network for Cervical Squamous Cell Carcinoma Histopathological Images

Accurately extracting lesion regions from histopathological images is crucial for diagnosing cervical squamous cell carcinoma, and determining the locations and shapes of such lesions is very important for evaluating tumor size and metastasis trends. The existing deep learning-based methods for extracting lesion regions in histopathological images primarily employ attention mechanisms to focus on regions of interest or integrate multiscale features to enhance the attained segmentation performance. However, their accuracy remains suboptimal when histopathological images with indistinct or blurred edge features are being processed. Therefore, we propose a dense prediction framework for histopathological images that integrates visual texture pattern enhancement and region-aware topological semantics via a feature interaction fusion strategy, enabling precise lesion segmentation. Specifically, our approach addresses the challenge brought by indistinct edges in pathological images through a multiscale texture enhancement branch, which sharpens discriminative morphological patterns. To optimize the feature fusion process, we introduce a positive definite-constrained attention mechanism that facilitates multilevel interactions between texture-enhanced and topological semantic features. Additionally, a united possibility state-space-model module is further designed to extract robust region-level topological semantics, thereby enhancing the ability of TexSem-Net to comprehend complex structural relationships. Extensive experiments demonstrate that our method outperforms the state-of-the-art segmentation networks in terms of both accuracy and delineation precision.