PMR-Net: Parallel Multi-Resolution Encoder-Decoder Network Framework for Medical Image Segmentation

• URL: http://arxiv.org/abs/2409.12678v1
• Date: Thu, 19 Sep 2024 11:45:08 GMT
• Title: PMR-Net: Parallel Multi-Resolution Encoder-Decoder Network Framework for Medical Image Segmentation
• Authors: Xiaogang Du, Dongxin Gu, Tao Lei, Yipeng Jiao, Yibin Zou,
• Abstract summary: We propose a novel parallel multi-resolution encoder-decoder network, namely PMR-Net. The proposed PMR-Net can achieve more accurate segmentation results than state-of-the-art methods on five public available datasets.
• Score: 5.554987043653931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, encoder-decoder networks have focused on expanding receptive fields and incorporating multi-scale context to capture global features for objects of varying sizes. However, as networks deepen, they often discard fine spatial details, impairing precise object localization. Additionally, conventional decoders' use of interpolation for upsampling leads to a loss of global context, diminishing edge segmentation accuracy. To address the above problems, we propose a novel parallel multi-resolution encoder-decoder network, namely PMR-Net for short. First, we design a parallel multi-resolution encoder and a multi-resolution context encoder. The parallel multi-resolution encoder can extract and fuse multi-scale fine-grained local features in parallel for input images with different resolutions. The multi-resolution context encoder fuses the global context semantic features of different receptive fields from different encoder branches to maintain effectively the integrity of global information. Secondly, we design a parallel multi-resolution decoder symmetrical to the structure of parallel multi-resolution encoder. The decoder can continuously supplement the global context features of low-resolution branches to the feature maps of high-resolution branches, and effectively solve the problem of global context feature loss caused by upsampling operation in the decoding process. Extensive experiment results demonstrate that our proposed PMR-Net can achieve more accurate segmentation results than state-of-the-art methods on five public available datasets. Moreover, PMR-Net is also a flexible network framework, which can meet the requirements of different scenarios by adjusting the number of network layers and the number of parallel encoder-decoder branches.

Related papers

• Multi-view Aggregation Network for Dichotomous Image Segmentation [76.75904424539543]
  Dichotomous Image (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet) Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-04-11T03:00:00Z)
• Towards Diverse Binary Segmentation via A Simple yet General Gated Network [71.19503376629083]
  We propose a simple yet general gated network (GateNet) to tackle binary segmentation tasks. With the help of multi-level gate units, the valuable context information from the encoder can be selectively transmitted to the decoder. We introduce a "Fold" operation to improve the atrous convolution and form a novel folded atrous convolution.
  arXiv  Detail & Related papers  (2023-03-18T11:26:36Z)
• LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention [0.0]
  We propose a projection-based semantic segmentation network called LENet with an encoder-decoder structure for LiDAR-based semantic segmentation. The encoder is composed of a novel multi-scale convolutional attention (MSCA) module with varying receptive field sizes to capture features. We show that our proposed method is lighter, more efficient, and robust compared to state-of-the-art semantic segmentation methods.
  arXiv  Detail & Related papers  (2023-01-11T02:51:38Z)
• Attention guided global enhancement and local refinement network for semantic segmentation [5.881350024099048]
  A lightweight semantic segmentation network is developed using the encoder-decoder architecture. A Global Enhancement Method is proposed to aggregate global information from high-level feature maps. A Local Refinement Module is developed by utilizing the decoder features as the semantic guidance. The two methods are integrated into a Context Fusion Block, and based on that, a novel Attention guided Global enhancement and Local refinement Network (AGLN) is elaborately designed.
  arXiv  Detail & Related papers  (2022-04-09T02:32:24Z)
• Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation [5.281694565226513]
  We apply contrastive learning to enhance the discriminative power of the multi-scale features extracted by semantic segmentation networks. By first mapping the encoder's multi-scale representations to a common feature space, we instantiate a novel form of supervised local-global constraint.
  arXiv  Detail & Related papers  (2022-03-25T01:24:24Z)
• Fractal Pyramid Networks [3.7384509727711923]
  We propose a new network architecture, the Fractal Pyramid Networks (PFNs) for pixel-wise prediction tasks. PFNs hold multiple information processing pathways and encode the information to multiple separate small-channel features. Our models can compete or outperform the state-of-the-art methods on the KITTI dataset with much fewer parameters.
  arXiv  Detail & Related papers  (2021-06-28T13:15:30Z)
• LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation [52.63874513999119]
  Cross-resolution image alignment is a key problem in multiscale giga photography. Existing deep homography methods neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. We propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs.
  arXiv  Detail & Related papers  (2021-06-08T02:51:45Z)
• Transformer Meets DCFAM: A Novel Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images [6.171417925832851]
  We introduce the Swin Transformer as the backbone to fully extract the context information. We also design a novel decoder named densely connected feature aggregation module (DCFAM) to restore the resolution and generate the segmentation map.
  arXiv  Detail & Related papers  (2021-04-25T11:34:22Z)
• A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection [74.88284082187462]
  One common strategy is to adopt dilated convolutions in the backbone networks to extract high-resolution feature maps. We propose one novel holistically-guided decoder which is introduced to obtain the high-resolution semantic-rich feature maps.
  arXiv  Detail & Related papers  (2020-12-18T10:51:49Z)
• Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding [59.48857453699463]
  In sequence-to-sequence learning, the decoder relies on the attention mechanism to efficiently extract information from the encoder. Recent work has proposed to use representations from different encoder layers for diversified levels of information. We propose layer-wise multi-view decoding, where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences.
  arXiv  Detail & Related papers  (2020-05-16T20:00:39Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.