U-MAN: U-Net with Multi-scale Adaptive KAN Network for Medical Image Segmentation
This work addresses segmentation challenges for medical imaging, offering incremental improvements over existing methods.
The paper tackled the problem of medical image segmentation by addressing limitations in U-Net architectures, such as semantic gaps and lack of multi-scale feature extraction, resulting in U-MAN outperforming state-of-the-art methods on datasets like BUSI, GLAS, and CVC with improved boundary accuracy and detail preservation.
Medical image segmentation faces significant challenges in preserving fine-grained details and precise boundaries due to complex anatomical structures and pathological regions. These challenges primarily stem from two key limitations of conventional U-Net architectures: (1) their simple skip connections ignore the encoder-decoder semantic gap between various features, and (2) they lack the capability for multi-scale feature extraction in deep layers. To address these challenges, we propose the U-Net with Multi-scale Adaptive KAN (U-MAN), a novel architecture that enhances the emerging Kolmogorov-Arnold Network (KAN) with two specialized modules: Progressive Attention-Guided Feature Fusion (PAGF) and the Multi-scale Adaptive KAN (MAN). Our PAGF module replaces the simple skip connection, using attention to fuse features from the encoder and decoder. The MAN module enables the network to adaptively process features at multiple scales, improving its ability to segment objects of various sizes. Experiments on three public datasets (BUSI, GLAS, and CVC) show that U-MAN outperforms state-of-the-art methods, particularly in defining accurate boundaries and preserving fine details.