TL;DR
SMG-Mamba provides a highly efficient and accurate solution for text-guided medical image segmentation by mapping specific modules to computational efficiency, boundary preservation, and deep semantic alignment, and successfully balances computational overhead with precise, fine-grained cross-modal feature fusion.
GLNet adopts a dual-branch encoder that combines a CNN-based Local Detail Perception Branch with a Mamba-based Global Context Modeling Branch, enabling the joint extraction of fine-grained local features and long-range semantic representations.
Experiments demonstrate that the proposed cross-layer semantic alignment mechanism outperforms mainstream approaches in terms of Dice, HD95, and Intersection over Union metrics, validating the effectiveness of the cross-layer semantic alignment mechanism for complex medical image segmentation tasks.
MGA-UNet, a frequency-aware multi-scale encoder–decoder segmentation framework that integrates wavelet-based frequency decomposition with Mamba-based long-range dependency modelling, demonstrates that frequency-domain decomposition and state-space modelling can complement each other for accurate medical image segmentat...
A tri-stream interaction paradigm replacing symmetric skip connections with directional fusion among semantic, spatial, and decoder-propagated streams at each decoding stage, and a Global Prototype Bank that captures dataset-level anatomical regularities via attention-based retrieval and gated EMA updates, providing pe...
Introduction State Space Models (SSMs) have demonstrated strong potential for 3D brain tumor segmentation owing to their linear computational complexity. However, conventional Mamba-based models are often limited by spectral bias, which favors low-frequency information while overlooking high-frequency boundary details,...
Accurate segmentation of colorectal polyps in colonoscopy images is crucial for the early detection and prevention of colorectal cancer (CRC). However, polyp segmentation remains challenging due to variations in size, shape, texture, and boundary ambiguity caused by specular highlights, motion blur, and low contrast. I...
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