PARF-Net: integrating pixel-wise adaptive receptive fields into hybrid Transformer-CNN network for medical image segmentation
- URL: http://arxiv.org/abs/2501.02882v1
- Date: Mon, 06 Jan 2025 09:48:35 GMT
- Title: PARF-Net: integrating pixel-wise adaptive receptive fields into hybrid Transformer-CNN network for medical image segmentation
- Authors: Xu Ma, Mengsheng Chen, Junhui Zhang, Lijuan Song, Fang Du, Zhenhua Yu,
- Abstract summary: We develop a new method to integrate convolutions of Pixel-wise Adaptive Receptive Fields (Conv-PARF) into hybrid Network for medical image segmentation.
Our method achieves 84.27% mean Dice on the dataset, surpassing existing methods by a large margin.
- Score: 5.896243816988129
- License:
- Abstract: Convolutional neural networks (CNNs) excel in local feature extraction while Transformers are superior in processing global semantic information. By leveraging the strengths of both, hybrid Transformer-CNN networks have become the major architectures in medical image segmentation tasks. However, existing hybrid methods still suffer deficient learning of local semantic features due to the fixed receptive fields of convolutions, and also fall short in effectively integrating local and long-range dependencies. To address these issues, we develop a new method PARF-Net to integrate convolutions of Pixel-wise Adaptive Receptive Fields (Conv-PARF) into hybrid Network for medical image segmentation. The Conv-PARF is introduced to cope with inter-pixel semantic differences and dynamically adjust convolutional receptive fields for each pixel, thus providing distinguishable features to disentangle the lesions with varying shapes and scales from the background. The features derived from the Conv-PARF layers are further processed using hybrid Transformer-CNN blocks under a lightweight manner, to effectively capture local and long-range dependencies, thus boosting the segmentation performance. By assessing PARF-Net on four widely used medical image datasets including MoNuSeg, GlaS, DSB2018 and multi-organ Synapse, we showcase the advantages of our method over the state-of-the-arts. For instance, PARF-Net achieves 84.27% mean Dice on the Synapse dataset, surpassing existing methods by a large margin.
Related papers
- TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting [6.987177704136503]
High-resolution images are preferable in medical imaging domain as they significantly improve the diagnostic capability of the underlying method.
Most of the existing deep learning-based techniques for medical image segmentation are optimized for input images having small spatial dimensions and perform poorly on high-resolution images.
We propose a parallel-in-branch architecture called TransResNet, which incorporates Transformer and CNN in a parallel manner to extract features from multi-resolution images independently.
arXiv Detail & Related papers (2024-10-01T18:22:34Z) - Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions.
We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training.
Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
arXiv Detail & Related papers (2023-10-22T02:27:02Z) - Mutual-Guided Dynamic Network for Image Fusion [51.615598671899335]
We propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs.
Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks.
arXiv Detail & Related papers (2023-08-24T03:50:37Z) - ConvFormer: Combining CNN and Transformer for Medical Image Segmentation [17.88894109620463]
We propose a hierarchical CNN and Transformer hybrid architecture, called ConvFormer, for medical image segmentation.
Our ConvFormer, trained from scratch, outperforms various CNN- or Transformer-based architectures, achieving state-of-the-art performance.
arXiv Detail & Related papers (2022-11-15T23:11:22Z) - Optimizing Vision Transformers for Medical Image Segmentation and
Few-Shot Domain Adaptation [11.690799827071606]
We propose Convolutional Swin-Unet (CS-Unet) transformer blocks and optimise their settings with relation to patch embedding, projection, the feed-forward network, up sampling and skip connections.
CS-Unet can be trained from scratch and inherits the superiority of convolutions in each feature process phase.
Experiments show that CS-Unet without pre-training surpasses other state-of-the-art counterparts by large margins on two medical CT and MRI datasets with fewer parameters.
arXiv Detail & Related papers (2022-10-14T19:18:52Z) - Semantic Labeling of High Resolution Images Using EfficientUNets and
Transformers [5.177947445379688]
We propose a new segmentation model that combines convolutional neural networks with deep transformers.
Our results demonstrate that the proposed methodology improves segmentation accuracy compared to state-of-the-art techniques.
arXiv Detail & Related papers (2022-06-20T12:03:54Z) - 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) - CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning.
The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery.
The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
arXiv Detail & Related papers (2021-12-31T04:37:11Z) - 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) - AINet: Association Implantation for Superpixel Segmentation [82.21559299694555]
We propose a novel textbfAssociation textbfImplantation (AI) module to enable the network to explicitly capture the relations between the pixel and its surrounding grids.
Our method could not only achieve state-of-the-art performance but maintain satisfactory inference efficiency.
arXiv Detail & Related papers (2021-01-26T10:40:13Z)
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.