ArticleCognitive computation2024
Large-Kernel Attention for 3D Medical Image Segmentation.
Article in Cognitive computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- A cloud-edge adaptive lightweight network with dynamic inference enables real-time ultrasound image segmentation.Scientific reports · 2026Article
- Privacy-preserving federated learning with light-weight attention improved CNNs for automated leukemia detection across distributed medical imaging.Scientific reports · 2026Article
- Clinical Application of Deep Learning for Spine MRI Interpretation: A Multicenter Evaluation of Artificial-Intelligence-Assisted versus Manual Reading on Diagnostic Agreement with the Reference Standard.Research (Washington, D.C.) · 2026Article
- LBD-PointNet++: a point cloud segmentation network for phenotypic trait extraction of broccoli seedlings.Frontiers in plant science · 2026Article
- Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Fully automated coronary artery calcium score and risk categorization from chest CT using deep learning and multiorgan segmentation: A validation study from National Lung Screening Trial (NLST).International journal of cardiology. Heart & vasculature · 2025Article
- LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation.PloS one · 2025Article
- Ultrasound image denoising autoencoder model based on lightweight attention mechanism.Quantitative imaging in medicine and surgery · 2024Article
- Scaling up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation.Neural computing & applications · 2023Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Automated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.
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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.