mlsanityData to 7 Oct 2026

Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)

arXiv 2604.08893 ↗First posted 10 Apr 2026cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Glioma is a harmful brain tumor that requires early detection to ensure better health results. Early detection of this tumor is key for effective treatment and requires an automated segmentation process. However, it is a challenging task to find tumors due to tumor characteristics like location and size. A reliable method to accurately separate tumor zones from healthy tissues is deep learning models, which have shown promising results over the last few years. In this research, an Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS) is introduced. This model is an innovative combination of adaptive dual residual networks, attention mechanisms, and multiscale spatial attention. The dual adaptive residual network architecture captures high-level semantic and intricate low-level details from brain images, ensuring precise segmentation of different tumor parts, types, and hard regions. The attention gates use gating and input signals to compute attention coefficients for the input features, and multiscale spatial attention generates scaled attention maps and combines these features to hold the most significant information about the brain tumor. We trained the model for 200 epochs using the ReLU activation function on BraTS 2020 and BraTS 2019 datasets. These improvements resulted in high accuracy for tumor detection and segmentation on BraTS 2020, achieving dice scores of 0.9229 for the whole tumor, 0.8432 for the tumor core, and 0.8004 for the enhancing tumor.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 57 references that are in the map. First posted on arXiv 10 Apr 2026.

Builds on

19 of 57 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2017Squeeze-and-Excitation Networkscited by 4,763
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  6. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  7. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  8. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  9. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  10. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  11. 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 271
  12. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
7 more
  1. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  2. 2020A survey of loss functions for semantic segmentationcited by 216
  3. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
  4. 2017A Review on Deep Learning Techniques Applied to Semantic Segmentationcited by 122
  5. 2023Attention Mechanisms in Medical Image Segmentation: A Surveycited by 9
  6. 2021Deep Learning-based Type Identification of Volumetric MRI Sequencescited by 4
  7. 2022ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentationcited by 4

Built on by

Not cited yet.