mlsanityData to 7 Oct 2026

A lightweight Convolutional Neural Network based on U shape structure and Attention Mechanism for Anterior Mediastinum Segmentation

arXiv 2411.01019 ↗First posted 1 Nov 2024eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

To automatically detect Anterior Mediastinum Lesions (AMLs) in the Anterior Mediastinum (AM), the primary requirement will be an automatic segmentation model specifically designed for the AM. The prevalence of AML is extremely low, making it challenging to conduct screening research similar to lung cancer screening. Retrospectively reviewing chest CT scans over a specific period to investigate the prevalence of AML requires substantial time. Therefore, developing an Artificial Intelligence (AI) model to find location of AM helps radiologist to enhance their ability to manage workloads and improve diagnostic accuracy for AMLs. In this paper, we introduce a U-shaped structure network to segment AM. Two attention mechanisms were used for maintaining long-range dependencies and localization. In order to have the potential of Multi-Head Self-Attention (MHSA) and a lightweight network, we designed a parallel MHSA named Wide-MHSA (W-MHSA). Maintaining long-range dependencies is crucial for segmentation when we upsample feature maps. Therefore, we designed a Dilated Depth-Wise Parallel Path connection (DDWPP) for this purpose. In order to design a lightweight architecture, we introduced an expanding convolution block and combine it with the proposed W-MHSA for feature extraction in the encoder part of the proposed U-shaped network. The proposed network was trained on 2775 AM cases, which obtained an average Dice Similarity Coefficient (DSC) of 87.83%, mean Intersection over Union (IoU) of 79.16%, and Sensitivity of 89.60%. Our proposed architecture exhibited superior segmentation performance compared to the most advanced segmentation networks, such as Trans Unet, Attention Unet, Res Unet, and Res Unet++.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 38 references that are in the map. First posted on arXiv 1 Nov 2024.

Builds on

18 of 38 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  6. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  7. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  8. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  9. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  10. 2021LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inferencecited by 475
  11. 2018SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networkscited by 445
  12. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 260
6 more
  1. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
  2. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  3. 2021LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentationcited by 85
  4. 2021U-Net Transformer: Self and Cross Attention for Medical Image Segmentationcited by 81
  5. 2015Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregationcited by 22
  6. 2023Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatmentscited by 2

Built on by

Not cited yet.