ArticleBrainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)2021
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.
Article in Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.Journal of imaging informatics in medicine · 2025Pooled it
- U-Swing: An Adaptive U-Net and Swin Fusion for WB-MRI Whole Spine Bone Marrow Segmentation.Journal of imaging informatics in medicine · 2026Article
- Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast.Npj imaging · 2026Review
- BRAIN-META: A reproducible CNN-vision transformer meta-ensemble pipeline for explainable brain tumor classification.MethodsX · 2026Article
- YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.Scientific reports · 2026Article
- NeXtSwin-X: dual-branch cross-attention fusion of ConvNeXt and swin transformer for accurate brain tumor classification from MRI and CT.Scientific reports · 2026Article
- Dynamic expert routing with boundary-aware decoding for accurate brain tumor segmentation from incomplete MRI data.Scientific reports · 2026Article
- Multi-class classification of brain tumor using a ResNet101 backbone integrated with multi-scale deformable attention module and advanced data augmentations.Scientific reports · 2026Article
- A Review of U-Net Based Deep Learning Frameworks for MRI-Based Brain Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2026Review
- Application of TransUnet Deep Learning Model for Automatic Segmentation of Cervical Cancer in Small-Field T2WI Images.Journal of imaging informatics in medicine · 2025Article
- Data-Oriented Octree Inverse Hierarchical Order Aggregation Hybrid Transformer-CNN for 3D Medical Segmentation.Journal of imaging informatics in medicine · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping.Scientific reports · 2025Article
- CAML-PSPNet: A Medical Image Segmentation Network Based on Coordinate Attention and a Mixed Loss Function.Sensors (Basel, Switzerland) · 2025Article
- Transformers for Neuroimage Segmentation: Scoping Review.Journal of medical Internet research · 2025Article
- Real-time colonoscopic detection and precise segmentation of colorectal polyps via PESNet.Frontiers in oncology · 2025Article
- LIU-NET: lightweight Inception U-Net for efficient brain tumor segmentation from multimodal 3D MRI images.PeerJ. Computer science · 2025Article
- Brain tumor segmentation by combining MultiEncoder UNet with wavelet fusion.Journal of applied clinical medical physics · 2024Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Applications of Deep Neural Networks with Fractal Structure and Attention Blocks for 2D and 3D Brain Tumor Segmentation.Journal of statistical theory and practice · 2024Article
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2 authors.
Funding
Abstract
Convolutional neural networks (CNNs) have achieved remarkable success in automatically segmenting organs or lesions on 3D medical images. Recently, vision transformer networks have exhibited exceptional performance in 2D image classification tasks. Compared with CNNs, transformer networks have an appealing advantage of extracting long-range features due to their self-attention algorithm. Therefore, we propose a CNN-Transformer combined model, called BiTr-Unet, with specific modifications for brain tumor segmentation on multi-modal MRI scans. Our BiTr-Unet achieves good performance on the BraTS2021 validation dataset with median Dice score 0.9335, 0.9304 and 0.8899, and median Hausdor_ distance 2.8284, 2.2361 and 1.4142 for the whole tumor, tumor core, and enhancing tumor, respectively. On the BraTS2021 testing dataset, the corresponding results are 0.9257, 0.9350 and 0.8874 for Dice score, and 3, 2.2361 and 1.4142 for Hausdorff distance. The code is publicly available at https://github.com/JustaTinyDot/BiTr-Unet.
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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.