Abstract
Previously, Multi-Layer Perceptrons (MLPs) were primarily used in image classification tasks. The emergence of the MLP-Mixer architecture has demonstrated the continued efficacy of MLPs in other visual tasks. To obtain superior results, it is imperative to have pre-trained weights from large datasets, and the Cross-Location (Token Mix) operation must be adaptively modified to suit the specific task at hand. Inspired by this, we proposed AMG-Mixer, an MLP-based architecture for image segmentation. In particular, recognizing the importance of positional information, we proposed AxialMBconv Token Mix utilizing Axial Attention. Additionally, to reduce Axial Attention’s receptive field constraints, we proposed Multi-scale Multi-axis MLP Gated (MS-MAMG) block which employs Multi-Axis MLP. The proposed AMG-Mixer architecture outperformed State-of-the-Art (SOTA) methods on benchmark datasets including GLaS, Data Science Bowl 2018, and Skin Lesion Segmentation ISIC 2018, even without pre-training. The proposed AMG-Mixer architecture has been confirmed effective and high performing in our study. The code is available at https://github.com/quanglets1fvr/amg_mixer
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This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2021.34.
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Le, HMQ., Le, TK., Pham, VT., Tran, TT. (2023). AMG-Mixer: A Multi-Axis Attention MLP-Mixer Architecture for Biomedical Image Segmentation. In: Nguyen, N.T., Le-Minh, H., Huynh, CP., Nguyen, QV. (eds) The 12th Conference on Information Technology and Its Applications. CITA 2023. Lecture Notes in Networks and Systems, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-031-36886-8_14
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