mlsanityData to 7 Oct 2026

Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net) for 3D Medical Image Segmentation and Visualization

arXiv 2406.17080 ↗First posted 24 Jun 2024eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Vision Transformers have shown superior performance to the traditional convolutional-based frameworks in many vision applications, including but not limited to the segmentation of 3D medical images. To further advance this area, this study introduces the Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net), which integrates the output of Swin Transformers and their corresponding convolutional blocks using 3D fusion blocks. The Multi-Aperture incorporates each image patch at its original resolutions with its pyramid representation to better preserve minute details. The proposed architecture has demonstrated a score of 89.73 and 7.31 for Dice and HD95, respectively, on the Synapse multi-organs dataset an improvement over the published results. The improved performance also comes with the added benefits of the reduced complexity of approximately 40 million parameters. Our code is available at https://github.com/Siyavashshabani/MFTC-Net

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 28 references that are in the map. First posted on arXiv 24 Jun 2024.

Builds on

22 of 28 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2017Squeeze-and-Excitation Networkscited by 4,763
  5. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  6. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  7. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  8. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  9. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  10. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
  11. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  12. 2016SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioningcited by 219
10 more
  1. 2021nnFormer: Interleaved Transformer for Volumetric Segmentationcited by 179
  2. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  3. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  4. 2021Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentationcited by 88
  5. 2021LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentationcited by 85
  6. 2019Attention Based Glaucoma Detection: A Large-scale Database and CNN Modelcited by 75
  7. 2021AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentationcited by 48
  8. 20233D TransUNet: Advancing Medical Image Segmentation through Vision Transformerscited by 20
  9. 2023MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decodercited by 12
  10. 2024MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentationcited by 2

Built on by

Not cited yet.