mlsanityData to 14 Oct 2026

All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images

arXiv 2312.10483 ↗First posted 16 Dec 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

Intracranial hemorrhages in head CT scans serve as a first line tool to help specialists diagnose different types. However, their types have diverse shapes in the same type but similar confusing shape, size and location between types. To solve this problem, this paper proposes an all attention U-Net. It uses channel attentions in the U-Net encoder side to enhance class specific feature extraction, and space and channel attentions in the U-Net decoder side for more accurate shape extraction and type classification. The simulation results show up to a 31.8% improvement compared to baseline, ResNet50 + U-Net, and better performance than in cases with limited attention.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 16 Dec 2023.

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16 of 24 references.

References · 16

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4 more
  1. 2018A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentationcited by 172
  2. 2019Multi-scale self-guided attention for medical image segmentationcited by 84
  3. 2017Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networkscited by 28
  4. 2017Memory-Efficient Implementation of DenseNetscited by 17

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