mlsanityData to 14 Oct 2026

CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation

arXiv 2311.04942 ↗First posted 8 Nov 2023eess.IV cs.CVcited by 7

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Abstract (arXiv)

A large portion of volumetric medical data, especially magnetic resonance imaging (MRI) data, is anisotropic, as the through-plane resolution is typically much lower than the in-plane resolution. Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information. Insufficient work has been done on 2.5D methods, in which 2D convolution is mainly used in concert with volumetric information. These models focus on learning the relationship across slices, but typically have many parameters to train. We offer a Cross-Slice Attention Module (CSAM) with minimal trainable parameters, which captures information across all the slices in the volume by applying semantic, positional, and slice attention on deep feature maps at different scales. Our extensive experiments using different network architectures and tasks demonstrate the usefulness and generalizability of CSAM. Associated code is available at https://github.com/aL3x-O-o-Hung/CSAM.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 30 of its 51 references that are in the map. First posted on arXiv 8 Nov 2023.

Builds on

30 of 51 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2024Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Networkcited by 6
  2. 2024Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detectioncited by 2
  3. 2025MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentationcited by 1
  4. 2025XAG-Net: A Cross-Slice Attention and Skip Gating Network for 2.5D Femur MRI Segmentationcited by 0
  5. 2025KLO-Net: A Dynamic K-NN Attention U-Net with CSP Encoder for Efficient Prostate Gland Segmentation from MRIcited by 0
  6. 2026LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generatorscited by 0
  7. 2026G^2RA-NET: Graph-based Cross-Slice Relation Modeling with Attention Gating for Medical Image Segmentationcited by 0