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D2-MLP: Dynamic Decomposed MLP Mixer for Medical Image Segmentation

arXiv 2409.08905 ↗First posted 13 Sep 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

Convolutional neural networks are widely used in various segmentation tasks in medical images. However, they are challenged to learn global features adaptively due to the inherent locality of convolutional operations. In contrast, MLP Mixers are proposed as a backbone to learn global information across channels with low complexity. However, they cannot capture spatial features efficiently. Additionally, they lack effective mechanisms to fuse and mix features adaptively. To tackle these limitations, we propose a novel Dynamic Decomposed Mixer module. It is designed to employ novel Mixers to extract features and aggregate information across different spatial locations and channels. Additionally, it employs novel dynamic mixing mechanisms to model inter-dependencies between channel and spatial feature representations and to fuse them adaptively. Subsequently, we incorporate it into a U-shaped Transformer-based architecture to generate a novel network, termed the Dynamic Decomposed MLP Mixer. We evaluated it for medical image segmentation on two datasets, and it achieved superior segmentation performance than other state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 13 Sep 2024.

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24 of 33 references.

References · 24

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  4. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,441
  5. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  6. 2021The Medical Segmentation Decathloncited by 620
  7. 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
  8. 2021Dynamic Neural Networks: A Surveycited by 405
  9. 2019Selective Kernel Networkscited by 401
  10. 2019Dynamic Convolution: Attention over Convolution Kernelscited by 355
  11. 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 271
  12. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 222
12 more
  1. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  2. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
  3. 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
  4. 2022HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentationcited by 94
  5. 2021Medical Image Segmentation Using Squeeze-and-Expansion Transformerscited by 56
  6. 2023Directional Connectivity-based Segmentation of Medical Imagescited by 30
  7. 2022Beyond Fixation: Dynamic Window Visual Transformercited by 28
  8. 2023Strip-MLP: Efficient Token Interaction for Vision MLPcited by 12
  9. 2024D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentationcited by 9
  10. 2024Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentationcited by 3
  11. 2024AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentationcited by 3
  12. 20223D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volumecited by 2

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