DPCTN: Dual path context-aware transformer network for medical image segmentation

Song, Pengfei; Yang, Zhe; Li, Jinjiang; Fan, Hui

doi:10.1016/j.engappai.2023.106634

Cited by 14 publications

(2 citation statements)

References 37 publications

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“…CT-Net [45] utilized an asymmetric asynchronous branch parallel structure to efficiently extract local and global representations while reducing unnecessary computational costs. DPCTN [46] combined the dual-branch fusion of a CNN and Transformer. To reduce the information loss during the information pooling process, DPCTN specially adopted a three-branch transposed self-attention module to significantly improve the segmentation performance.…”

Section: Transformermentioning

confidence: 99%

CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation

Chen,

Yuan

2024

Algorithms

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The Transformer architecture has gained widespread acceptance in image segmentation. However, it sacrifices local feature details and necessitates extensive data for training, posing challenges to its integration into computer-aided medical image segmentation. To address the above challenges, we introduce CCFNet, a collaborative cross-fusion network, which continuously fuses a CNN and Transformer interactively to exploit context dependencies. In particular, when integrating CNN features into Transformer, the correlations between local and global tokens are adaptively fused through collaborative self-attention fusion to minimize the semantic disparity between these two types of features. When integrating Transformer features into the CNN, it uses the spatial feature injector to reduce the spatial information gap between features due to the asymmetry of the extracted features. In addition, CCFNet implements the parallel operation of Transformer and the CNN and independently encodes hierarchical global and local representations when effectively aggregating different features, which can preserve global representations and local features. The experimental findings from two public medical image segmentation datasets reveal that our approach exhibits competitive performance in comparison to current state-of-the-art methods.

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Section: Transformermentioning

confidence: 99%

CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation

Chen,

Yuan

2024

Algorithms

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“…Instance Segmentation with Transformer. In the realm of 2D instance segmentation, the power of Transformers [49] has been harnessed in several state-of-the-art works. For instance, DETR [20] has demonstrated superior performance for various vision tasks [41,7], owing to the Transformer's inherent capability to model long-range dependencies which is beneficial for handling complex scenes.…”

Section: Related Workmentioning

confidence: 99%

Potential development of dairy production in Sichuan Province, China

Pan

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Most existing 3D instance segmentation methods are derived from 3D semantic segmentation models. However, these indirect approaches suffer from certain limitations. They fail to fully leverage global and local semantic information for accurate prediction, which hampers the overall performance of the 3D instance segmentation framework. To address these issues, this paper presents PSGformer, a novel 3D instance segmentation network. PSGformer incorporates two key advancements to enhance the performance of 3D instance segmentation. Firstly, we propose a Multi-Level Semantic Aggregation Module, which effectively captures scene features by employing foreground point filtering and multi-radius aggregation. This module enables the acquisition of more detailed semantic information from global and local perspectives. Secondly, PSGformer introduces a Parallel Feature Fusion Transformer Module that independently processes super-point features and aggregated features using transformers. The model achieves a more comprehensive feature representation by the features which connect global and local features. We conducted extensive experiments on the ScanNetv2 dataset. Notably, PSGformer exceeds compared state-of-the-art methods by 2.2% on ScanNetv2 hidden test set in terms of mAP. Our code and models will be publicly released.

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