mlsanityData to 7 Oct 2026

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

arXiv 2211.16950 ↗First posted 30 Nov 2022eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 30 Nov 2022.

Builds on

26 of 35 references.

References · 26

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2017Squeeze-and-Excitation Networkscited by 4,763
  6. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  7. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  8. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  9. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  10. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  11. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  12. 2021Scaling Vision Transformerscited by 993
14 more
  1. 2018CCNet: Criss-Cross Attention for Semantic Segmentationcited by 717
  2. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
  3. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  4. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  5. 2019GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyondcited by 331
  6. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 331
  7. 2021Camouflaged Object Segmentation with Distraction Miningcited by 151
  8. 2019Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentationcited by 55
  9. 2022DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentationcited by 37
  10. 2022FCN-Transformer Feature Fusion for Polyp Segmentationcited by 37
  11. 2022SSformer: A Lightweight Transformer for Semantic Segmentationcited by 13
  12. 2021GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentationcited by 11
  13. 2022ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic videocited by 11
  14. 2022Vision Transformers in Medical Imaging: A Reviewcited by 8

Built on by

Not cited yet.