ArticleBMC bioinformatics2023
EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- VentrEX: An Anatomically Guided Deep Learning Pipeline for Ventricular Segmentation in Cine Cardiac MRI.Journal of imaging · 2026Article
- Application of transformer models in medical image segmentation: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- MMSeg: Multi-scale Vision Mamba for Lightweight Generalizable Medical Image Segmentation.Journal of imaging informatics in medicine · 2026Article
- [Review of application of U-Net and Transformer in colon polyp image segmentation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- A histopathology aware DINO model with attention based representation enhancement.Scientific reports · 2025Article
- Automatic detection of arterial input function for brain DCE-MRI in multi-site cohorts.Magnetic resonance in medicine · 2025Article
- HCTG-Net: A Hybrid CNN-Transformer Network with Gated Fusion for Automatic ECG Arrhythmia Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- [A multi-scale feature capturing and spatial position attention model for colorectal polyp image segmentation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Transformer-assisted broad learning for hybrid intelligence-based skin cancer segmentation.Scientific reports · 2025Article
- Development and verification of a convolutional neural network-based model for automatic mandibular canal localization on multicenter CBCT images.BMC oral health · 2025Article
- SFMANet: A Spatial-Frequency multi-scale attention network for stroke lesion segmentation.Scientific reports · 2025Article
- A deep ensemble learning framework for glioma segmentation and grading prediction.Scientific reports · 2025Article
- SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers.Sensors (Basel, Switzerland) · 2024Review
- DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data.Proceedings of machine learning research · 2024Article
- A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing.Heliyon · 2024Article
- Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer.Cancers · 2024Article
- Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network.BMC ophthalmology · 2024Article
- Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images.PloS one · 2024Article
- UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.PloS one · 2024Article
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4 authors.
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Abstract
Although various methods based on convolutional neural networks have improved the performance of biomedical image segmentation to meet the precision requirements of medical imaging segmentation task, medical image segmentation methods based on deep learning still need to solve the following problems: (1) Difficulty in extracting the discriminative feature of the lesion region in medical images during the encoding process due to variable sizes and shapes; (2) difficulty in fusing spatial and semantic information of the lesion region effectively during the decoding process due to redundant information and the semantic gap. In this paper, we used the attention-based Transformer during the encoder and decoder stages to improve feature discrimination at the level of spatial detail and semantic location by its multihead-based self-attention. In conclusion, we propose an architecture called EG-TransUNet, including three modules improved by a transformer: progressive enhancement module, channel spatial attention, and semantic guidance attention. The proposed EG-TransUNet architecture allowed us to capture object variabilities with improved results on different biomedical datasets. EG-TransUNet outperformed other methods on two popular colonoscopy datasets (Kvasir-SEG and CVC-ClinicDB) by achieving 93.44% and 95.26% on mDice. Extensive experiments and visualization results demonstrate that our method advances the performance on five medical segmentation datasets with better generalization ability.
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