Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging

• URL: http://arxiv.org/abs/2404.18199v1
• Date: Sun, 28 Apr 2024 14:37:10 GMT
• Title: Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
• Authors: Fares Bougourzi, Fadi Dornaika, Abdelmalik Taleb-Ahmed, Vinh Truong Hoang,
• Abstract summary: We introduce a novel hybrid CNN-Transformer segmentation architecture (PAG-TransYnet) designed for efficiently building a strong CNN-Transformer encoder. Our approach exploits attention gates within a Dual Pyramid hybrid encoder.
• Score: 17.07490339960335
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Inspired by the success of Transformers in Computer vision, Transformers have been widely investigated for medical imaging segmentation. However, most of Transformer architecture are using the recent transformer architectures as encoder or as parallel encoder with the CNN encoder. In this paper, we introduce a novel hybrid CNN-Transformer segmentation architecture (PAG-TransYnet) designed for efficiently building a strong CNN-Transformer encoder. Our approach exploits attention gates within a Dual Pyramid hybrid encoder. The contributions of this methodology can be summarized into three key aspects: (i) the utilization of Pyramid input for highlighting the prominent features at different scales, (ii) the incorporation of a PVT transformer to capture long-range dependencies across various resolutions, and (iii) the implementation of a Dual-Attention Gate mechanism for effectively fusing prominent features from both CNN and Transformer branches. Through comprehensive evaluation across different segmentation tasks including: abdominal multi-organs segmentation, infection segmentation (Covid-19 and Bone Metastasis), microscopic tissues segmentation (Gland and Nucleus). The proposed approach demonstrates state-of-the-art performance and exhibits remarkable generalization capabilities. This research represents a significant advancement towards addressing the pressing need for efficient and adaptable segmentation solutions in medical imaging applications.

Related papers

• DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation [5.5582646801199225]
  This study proposes a novel deep medical image segmentation framework, called DA-TransUNet. It aims to integrate the Transformer and dual attention block(DA-Block) into the traditional U-shaped architecture. Unlike earlier transformer-based U-net models, DA-TransUNet utilizes Transformers and DA-Block to integrate not only global and local features, but also image-specific positional and channel features.
  arXiv  Detail & Related papers  (2023-10-19T08:25:03Z)
• 3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers [40.21263511313524]
  Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, has proven highly successful for various medical image segmentation tasks. To address these limitations, researchers have turned to Transformers, renowned for their global self-attention mechanisms.
  arXiv  Detail & Related papers  (2023-10-11T18:07:19Z)
• MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation [0.46040036610482665]
  MaxViT-UNet is a hybrid vision transformer (CNN-Transformer) for medical image segmentation. The proposed Hybrid Decoder is designed to harness the power of both the convolution and self-attention mechanisms at each decoding stage. The inclusion of multi-axis self-attention, within each decoder stage, significantly enhances the discriminating capacity between the object and background regions.
  arXiv  Detail & Related papers  (2023-05-15T07:23:54Z)
• Focused Decoding Enables 3D Anatomical Detection by Transformers [64.36530874341666]
  We propose a novel Detection Transformer for 3D anatomical structure detection, dubbed Focused Decoder. Focused Decoder leverages information from an anatomical region atlas to simultaneously deploy query anchors and restrict the cross-attention's field of view. We evaluate our proposed approach on two publicly available CT datasets and demonstrate that Focused Decoder not only provides strong detection results and thus alleviates the need for a vast amount of annotated data but also exhibits exceptional and highly intuitive explainability of results via attention weights.
  arXiv  Detail & Related papers  (2022-07-21T22:17:21Z)
• MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet [55.16833099336073]
  We propose to self-distill a Transformer-based UNet for medical image segmentation. It simultaneously learns global semantic information and local spatial-detailed features. Our MISSU achieves the best performance over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-02T07:38:53Z)
• PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation [7.140322699310487]
  We propose a novel hybrid architecture for medical image segmentation called PHTrans. PHTrans parallelly hybridizes Transformer and CNN in main building blocks to produce hierarchical representations from global and local features.
  arXiv  Detail & Related papers  (2022-03-09T08:06:56Z)
• DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation [18.755217252996754]
  We propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet) Unlike many prior Transformer-based solutions, the proposed DS-TransUNet first adopts dual-scale encoderworks based on Swin Transformer to extract the coarse and fine-grained feature representations of different semantic scales. As the core component for our DS-TransUNet, a well-designed Transformer Interactive Fusion (TIF) module is proposed to effectively establish global dependencies between features of different scales through the self-attention mechanism.
  arXiv  Detail & Related papers  (2021-06-12T08:37:17Z)
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation [63.46694853953092]
  Swin-Unet is an Unet-like pure Transformer for medical image segmentation. tokenized image patches are fed into the Transformer-based U-shaped decoder-Decoder architecture.
  arXiv  Detail & Related papers  (2021-05-12T09:30:26Z)
• CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation [95.51455777713092]
  Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. We propose a novel framework that efficiently bridges a bf Convolutional neural network and a bf Transformer bf (CoTr) for accurate 3D medical image segmentation.
  arXiv  Detail & Related papers  (2021-03-04T13:34:22Z)
• Medical Transformer: Gated Axial-Attention for Medical Image Segmentation [73.98974074534497]
  We study the feasibility of using Transformer-based network architectures for medical image segmentation tasks. We propose a Gated Axial-Attention model which extends the existing architectures by introducing an additional control mechanism in the self-attention module. To train the model effectively on medical images, we propose a Local-Global training strategy (LoGo) which further improves the performance.
  arXiv  Detail & Related papers  (2021-02-21T18:35:14Z)
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation [78.01570371790669]
  Medical image segmentation is an essential prerequisite for developing healthcare systems. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard. We propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation.
  arXiv  Detail & Related papers  (2021-02-08T16:10:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.