UC-Mamba: Adaptive Cross-Level Fusion Network With 2D-Selective Foveal Scanning and Channel-Gated Linear Unit for Ulcerative Colitis Evaluation.

Accurate evaluation of ulcerative colitis severity remains challenging due to complex lesion characteristics and limitations in existing deep learning approaches. This paper presents UC-Mamba, a novel neural network architecture integrating biomimetic design principles with advanced feature extraction capabilities for enhanced UC assessment. To address critical challenges in current methodologies, we propose three innovative components: (1) a 2D-Selective Foveal Scanning mechanism to overcome the limited sensitivity to near-focal features and weaker perception of distant features in existing scanning methods; (2) a Channel-Gated Linear Unit to enhance local feature modeling, addressing the lack of local inductive bias in most existing networks; and (3) an Adaptive Cross-level Fusion mechanism to effectively integrate cross-layer features and capture rich global contextual information. Comprehensive experiments are conducted on two distinct UC datasets. On private Dataset, UC-Mamba achieves superior performance with 88.47 % accuracy, 84.63 % Matthews Correlation Coefficient, and 87.92 % F1-score, significantly outperforming state-of-the-art methods. Similar improvements are demonstrated on the more challenging public Dataset Kvasir, reaching 85.02 % accuracy. Ablation studies validate the effectiveness of each proposed component, with the complete model exhibiting optimal performance through their synergistic integration. The source code is available at github.com/kititoo/UC-Mamba.