mlsanityData to 7 Oct 2026

DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner

arXiv 2304.09620 ↗First posted 19 Apr 2023eess.IV cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

The DCELANM-Net structure, which this article offers, is a model that ingeniously combines a Dual Channel Efficient Layer Aggregation Network (DCELAN) and a Micro Masked Autoencoder (Micro-MAE). On the one hand, for the DCELAN, the features are more effectively fitted by deepening the network structure; the deeper network can successfully learn and fuse the features, which can more accurately locate the local feature information; and the utilization of each layer of channels is more effectively improved by widening the network structure and residual connections. We adopted Micro-MAE as the learner of the model. In addition to being straightforward in its methodology, it also offers a self-supervised learning method, which has the benefit of being incredibly scaleable for the model.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 44 references that are in the map. First posted on arXiv 19 Apr 2023.

Builds on

35 of 44 references.

References · 35

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  6. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  7. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  8. 2016Densely Connected Convolutional Networkscited by 6,691
  9. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  10. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  11. 2017Squeeze-and-Excitation Networkscited by 4,763
  12. 2014Going Deeper with Convolutionscited by 4,231
23 more
  1. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  2. 2017Non-local Neural Networkscited by 2,290
  3. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  4. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  5. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  6. 2021SimMIM: A Simple Framework for Masked Image Modelingcited by 1,147
  7. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  8. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  9. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  10. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
  11. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  12. 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
  13. 2019F3Net: Fusion, Feedback and Focus for Salient Object Detectioncited by 176
  14. 2019An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detectioncited by 163
  15. 2016The Importance of Skip Connections in Biomedical Image Segmentationcited by 126
  16. 2020DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentationcited by 125
  17. 2022Revealing the Dark Secrets of Masked Image Modelingcited by 117
  18. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  19. 2018Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removalcited by 56
  20. 2022DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentationcited by 37
  21. 2020DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentationcited by 28
  22. 2022ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentationcited by 28
  23. 2018Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classificationcited by 7

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classificationcited by 2