[2508.01941] Less is More: AMBER-AFNO -- a New Benchmark for Lightweight 3D Medical Image Segmentation

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2508.01941 (eess) [Submitted on 3 Aug 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Less is More: AMBER-AFNO -- a New Benchmark for Lightweight 3D Medical Image Segmentation Authors:Andrea Dosi, Semanto Mondal, Rajib Chandra Ghosh, Massimo Brescia, Giuseppe Longo View a PDF of the paper titled Less is More: AMBER-AFNO -- a New Benchmark for Lightweight 3D Medical Image Segmentation, by Andrea Dosi and 4 other authors View PDF HTML (experimental) Abstract:We adapt the remote sensing-inspired AMBER model from multi-band image segmentation to 3D medical datacube segmentation. To address the computational bottleneck of the volumetric transformer, we propose the AMBER-AFNO architecture. This approach uses Adaptive Fourier Neural Operators (AFNO) instead of the multi-head self-attention mechanism. Unlike spatial pairwise interactions between tokens, global token mixing in the frequency domain avoids $\mathcal{O}(N^2)$ attention-weight calculations. As a result, AMBER-AFNO achieves quasi-linear computational complexity and linear memory scaling. This new way to model global context reduces reliance on dense transformers while preserving global contextual modeling capability. By using attention-free spectral operations, our design offers a compact parameterization and maintains a competitive computational complexity. We evaluate AMBER-AFNO on three public datasets: ACDC, Synapse, and BraT...