March 27, 2024, 4:46 a.m. | Kazi Shahriar Sanjid, Md. Tanzim Hossain, Md. Shakib Shahariar Junayed, Dr. Mohammad Monir Uddin

cs.CV updates on

arXiv:2403.17432v1 Announce Type: cross
Abstract: Integrating components from convolutional neural networks and state space models in medical image segmentation presents a compelling approach to enhance accuracy and efficiency. We introduce Mamba HUNet, a novel architecture tailored for robust and efficient segmentation tasks. Leveraging strengths from Mamba UNet and the lighter version of Hierarchical Upsampling Network (HUNet), Mamba HUNet combines convolutional neural networks local feature extraction power with state space models long range dependency modeling capabilities. We first converted HUNet into …

abstract accuracy architecture arxiv components convolutional neural networks eess.iv efficiency hierarchical image mamba medical multiple multiple sclerosis network networks neural networks novel robust segmentation semantic sequence model space state state space models tasks type

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