ArticleHeliyon2024
SCANeXt: Enhancing 3D medical image segmentation with dual attention network and depth-wise convolution.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Deep Learning-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images.Journal of imaging informatics in medicine · 2026Article
- CA-3DTransUNet with dynamic cross-scale fusion for pulmonary nodule segmentation.Scientific reports · 2026Article
- DiagNeXt: A Two-Stage Attention-Guided ConvNeXt Framework for Kidney Pathology Segmentation and Classification.Journal of imaging · 2025Article
- LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation.PloS one · 2025Article
- A novel LVPA-UNet network for target volume automatic delineation: An MRI case study of nasopharyngeal carcinoma.Heliyon · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Existing approaches to 3D medical image segmentation can be generally categorized into convolution-based or transformer-based methods. While convolutional neural networks (CNNs) demonstrate proficiency in extracting local features, they encounter challenges in capturing global representations. In contrast, the consecutive self-attention modules present in vision transformers excel at capturing long-range dependencies and achieving an expanded receptive field. In this paper, we propose a novel approach, termed
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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.