Title: Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Efficient Medical segmentation, multimodal learning, Knowledge Transfer

Abstract: Lightweight 3D medical image segmentation remains constrained by a fundamental "efficiency / robustness conflict'', particularly when processing complex anatomical structures and heterogeneous modalities. In this paper, we study how to redesign the framework based on the characteristics of high-dimensional 3D images, and explore data synergy to overcome the fragile representation of lightweight methods. Our approach, VeloxSeg, begins with a deployable and extensible dual-stream CNN-Transformer architecture composed of Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC). For each 3D image, we invoke a "glance-and-focus'' principle, where PWA rapidly retrieves multi-scale information, and JLC ensures robust local feature extraction with minimal parameters, significantly enhancing the model's ability to operate with low computational budget. Followed by an extension of the dual-stream architecture that incorporates modal interaction into the multi-scale image-retrieval process, VeloxSeg efficiently models heterogeneous modalities. Finally, Spatially Decoupled Knowledge Transfer (SDKT) via Gram matrices injects the texture prior extracted by a self-supervised network into the segmentation network, yielding stronger representations than baselines at no extra inference cost. Experimental results on multimodal benchmarks show that VeloxSeg achieves a 26\% Dice improvement, alongside increasing GPU throughput by 11$\times$ and CPU by 48$\times$.

TL;DR: This paper proposes VeloxSeg, a theory-based lightweight framework that systematically alleviates the ''efficiency / robustness conflict'' in 3D medical segmentation.