ArticleIEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision2024
CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation.
Article in IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Accurate 3D recording: Integrating ground-based LiDAR data and 3D segmentation network to extract 3D traits and analyze genetics in wheat populations.Plant phenomics (Washington, D.C.) · 2026Article
- Multidimensional Dual Encoding Network For Liver Lesion Classification From Multi-Phase Magnetic Resonance Imaging.Journal of imaging informatics in medicine · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Retinal layer segmentation in OCT images with a 2.5D cross-slice feature fusion module for glaucoma assessment.Biomedical optics express · 2026Article
- KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation.npj health systems · 2026Article
- Multi-attention Mechanism for Enhanced Pseudo-3D Prostate Zonal Segmentation.Journal of imaging informatics in medicine · 2025Article
- A Neural Conditional Random Field Model Using Deep Features and Learnable Functions for End-to-End MRI Prostate Zonal Segmentation.The journal of machine learning for biomedical imaging · 2025Article
- [A multi-feature fusion-based model for fetal orientation classification from intrapartum ultrasound videos].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Time-to-Event Pretraining for 3D Medical Imaging.... International Conference on Learning Representations · 2025Article
- Densely Populated Cell and Organelles Segmentation with U-Net Ensembles.bioRxiv : the preprint server for biology · 2025Article
- Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer VisionArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.