bibtext: @InProceedings{ Cha_EMNet_MICCAI2024,
                   author = { Chang, Ao and Zeng, Jiajun and Huang, Ruobing and Ni, Dong },
                   title = { { EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: Convolutional neural networks have primarily led 3D medical image segmentation but may be limited by small receptive fields.
  Transformer models excel in capturing global relationships through self-attention but are challenged by high computational costs at high resolutions. Recently, Mamba, a state space model, has emerged as an effective approach for sequential modeling. Inspired by its success, we introduce a novel Mamba-based 3D medical image segmentation model called EM-Net. It not only efficiently captures attentive interaction between regions by integrating and selecting channels, but also effectively utilizes frequency domain to harmonize the learning of features across varying scales, while accelerating training speed. Comprehensive experiments on two challenging multi-organ datasets with other state-of-the-art (SOTA) algorithms show that our method exhibits better segmentation accuracy while requiring nearly half the parameter size of SOTA models and 2x faster training speed. Our code is publicly available at https://github.com/zang0902/EM-Net.
title: EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation
authors: Chang, Ao Zeng, Jiajun Huang, Ruobing Ni, Dong