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Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation

arXiv 2305.07850 ↗First posted 13 May 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

Deep Learning based techniques have gained significance over the past few years in the field of medicine. They are used in various applications such as classifying medical images, segmentation and identification. The existing architectures such as UNet, Attention UNet and Attention Residual UNet are already currently existing methods for the same application of brain tumor segmentation, but none of them address the issue of how to extract the features in channel level. In this paper, we propose a new architecture called Squeeze Excitation Embedded Attention UNet (SEEA-UNet), this architecture has both Attention UNet and Squeeze Excitation Network for better results and predictions, this is used mainly because to get information at both Spatial and channel levels. The proposed model was compared with the existing architectures based on the comparison it was found out that for lesser number of epochs trained, the proposed model performed better. Binary focal loss and Jaccard Coefficient were used to monitor the model's performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 23 references that are in the map. First posted on arXiv 13 May 2023.

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18 of 23 references.

References · 18

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017Squeeze-and-Excitation Networkscited by 4,789
  3. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  4. 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
  5. 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
  6. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
  7. 2019Multi-scale self-guided attention for medical image segmentationcited by 84
  8. 2021Medical Image Segmentation Using Squeeze-and-Expansion Transformerscited by 57
  9. 2020KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representationscited by 49
  10. 2017CNN-based Segmentation of Medical Imaging Datacited by 48
  11. 2021Optimized U-Net for Brain Tumor Segmentationcited by 40
  12. 2022SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalitiescited by 31
6 more
  1. 2019USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasetscited by 27
  2. 2020Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Imagescited by 24
  3. 2020Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentationcited by 12
  4. 2020A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentationcited by 9
  5. 2020DR-Unet104 for Multimodal MRI brain tumor segmentationcited by 3
  6. 2021AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentationcited by 1

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