[2503.03141] Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2503.03141 (eess) [Submitted on 5 Mar 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation Authors:Chun-Wun Cheng, Yining Zhao, Yanqi Cheng, Javier A. Montoya-Zegarra, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero View a PDF of the paper titled Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation, by Chun-Wun Cheng and 5 other authors View PDF HTML (experimental) Abstract:Image segmentation is a fundamental task in both image analysis and medical applications. State-of-the-art methods predominantly rely on encoder-decoder architectures with a U-shaped design, commonly referred to as U-Net. Recent advancements integrating transformers and MLPs improve performance but still face key limitations, such as poor interpretability, difficulty handling intrinsic noise, and constrained expressiveness due to discrete layer structures, often lacking a solid theoretical this http URL this work, we introduce Implicit U-KAN 2.0, a novel U-Net variant that adopts a two-phase encoder-decoder structure. In the SONO phase, we use a second-order neural ordinary differential equation (NODEs), called the SONO block, for a more efficient, expressive, and theoretically grounded modeling approach. In the SONO-MultiKAN phase, we integrate the second-order NODEs and MultiKAN layer as the core comput...