DSNet: a simple yet efficient network with dual-stream attention for lesion segmentation

• URL: http://arxiv.org/abs/2211.16950v1
• Date: Wed, 30 Nov 2022 12:48:17 GMT
• Title: DSNet: a simple yet efficient network with dual-stream attention for lesion segmentation
• Authors: Yunxiao Liu
• Abstract summary: We propose a simple yet efficient network DSNet for lesion segmentation. Our method achieves SOTA performance in terms of mean Dice coefficient (mDice) and mean Intersection over Union (mIoU) with low model complexity and memory consumption.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lesion segmentation requires both speed and accuracy. In this paper, we propose a simple yet efficient network DSNet, which consists of a encoder based on Transformer and a convolutional neural network(CNN)-based distinct pyramid decoder containing three dual-stream attention (DSA) modules. Specifically, the DSA module fuses features from two adjacent levels through the false positive stream attention (FPSA) branch and the false negative stream attention (FNSA) branch to obtain features with diversified contextual information. We compare our method with various state-of-the-art (SOTA) lesion segmentation methods with several public datasets, including CVC-ClinicDB, Kvasir-SEG, and ISIC-2018 Task 1. The experimental results show that our method achieves SOTA performance in terms of mean Dice coefficient (mDice) and mean Intersection over Union (mIoU) with low model complexity and memory consumption.

Related papers

• DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication [50.017055360261665]
  We introduce DiffVein, a unified diffusion model-based framework which simultaneously addresses vein segmentation and authentication tasks. For better feature interaction between these two branches, we introduce two specialized modules. In this way, our framework allows for a dynamic interplay between diffusion and segmentation embeddings.
  arXiv  Detail & Related papers  (2024-02-03T06:49:42Z)
• BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation [11.986549780782724]
  We propose a hybrid yet effective CNN-Transformer network, named BRAU-Net++, for an accurate medical image segmentation task. Specifically, BRAU-Net++ uses bi-level routing attention as the core building block to design our u-shaped encoder-decoder structure. Our proposed approach surpasses other state-of-the-art methods including its baseline: BRAU-Net.
  arXiv  Detail & Related papers  (2024-01-01T10:49:09Z)
• Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation [12.812992773512871]
  We propose a new segmentation framework, named UDTransNet, to solve three semantic gaps in U-Net. Specifically, we propose a Dual Attention Transformer ( DAT) module for capturing the channel- and spatial-wise relationships, and a Decoder-guided Recalibration Attention (DRA) module for effectively connecting the DAT tokens and the decoder features. Our UDTransNet produces higher evaluation scores and finer segmentation results with relatively fewer parameters over the state-of-the-art segmentation methods on different public datasets.
  arXiv  Detail & Related papers  (2023-12-23T07:39:42Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Bandwidth-efficient distributed neural network architectures with application to body sensor networks [73.02174868813475]
  This paper describes a conceptual design methodology to design distributed neural network architectures. We show that the proposed framework enables up to a factor 20 in bandwidth reduction with minimal loss. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.
  arXiv  Detail & Related papers  (2022-10-14T12:35:32Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• 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)
• Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network [28.946037652152395]
  We identify the wide variation in the ratio between intra- and inter-slice resolutions as a crucial obstacle to the performance. We propose a slice-aware 2.5D network that emphasizes extracting discnative features utilizing not only in-plane semantics but also out-of-plane for each separate slice.
  arXiv  Detail & Related papers  (2022-03-07T14:31:26Z)
• Deep Convolutional Learning-Aided Detector for Generalized Frequency Division Multiplexing with Index Modulation [0.0]
  The proposed method first pre-processes the received signal by using a zero-forcing (ZF) detector and then uses a neural network consisting of a convolutional neural network (CNN) followed by a fully-connected neural network (FCNN) The FCNN part uses only two fully-connected layers, which can be adapted to yield a trade-off between complexity and bit error rate (BER) performance. It has been demonstrated that the proposed deep convolutional neural network-based detection and demodulation scheme provides better BER performance compared to ZF detector with a reasonable complexity increase.
  arXiv  Detail & Related papers  (2022-02-06T22:18:42Z)
• Learning to Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
  We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system. A common approach is to use the mean value as the estimate, motivated by channel hardening. We propose two novel estimation methods.
  arXiv  Detail & Related papers  (2021-09-06T13:42:32Z)
• MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling [66.01558025094333]
  We propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss.
  arXiv  Detail & Related papers  (2020-05-15T10:37:02Z)

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.