MaLViL: Multi-axis Low-rank Vision-LSTM for Medical Image Segmentation

2026-08-18

AI Summary

MaLViL通过多轴低秩Vision-LSTM网络解决医学图像分割中细节丢失问题，提高分割精度并减少内存消耗。

Abstract

Vision-LSTM (ViL) enables efficient global modeling, but its cost still scales with the number of spatial tokens, so existing segmenters confine ViL to a coarse bottleneck and lose fine anatomical detail. Rasterizing 2D features into a 1D sequence further breaks adjacency across the orthogonal scan axis. We propose MaLViL, a Multi-axis Low-rank Vision-LSTM network that extends ViL across decoder resolutions. Bidirectional low-rank ViL (Bi-LRViL) reasons on a compact orthonormal subspace and preserves detail through an orthogonal residual; scale-aware SaLViL restores cross-axis neighbors before serialization; and a Cross-Directional Mixer (CDM) fuses orthogonal horizontal and vertical traversal paths. Statistics-Guided Skip Modulation (SGSM) further retains boundary cues in encoder skips. On skin-lesion, ultrasound, and multi-organ CT benchmarks, MaLViL achieves competitive or state-of-the-art segmentation accuracy, while reducing ViL operator memory by up to $83\times$ at fine decoder resolutions. Code is available at: https://github.com/xmindflow/malvil.

Problem

• Vision-LSTM
• Medical Image Segmentation
• Spatial Tokens
• Anatomical Detail

Innovation

• Multi-axis Low-rank Vision-LSTM
• Bidirectional low-rank ViL
• Scale-aware SaLViL
• Cross-Directional Mixer
• Statistics-Guided Skip Modulation