ArticleBrainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)2020
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation.
Article in Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- 3D brain tumor segmentation using an improved V-Net architecture and 3D attention gate.Neuroimage. Reports · 2026Article
- MRI and Meningioma Research: Hotspots and Trends via Bibliometric AnalysisCurrent medical imaging · 2026Article
- Lightweight Brain Tumor Segmentation Through Wavelet-Guided Iterative Axial Factorization Attention.Brain sciences · 2025Article
- GAME-Net: an ensemble deep learning framework integrating Generative Autoencoders and attention mechanisms for automated brain tumor segmentation in MRI.Frontiers in computational neuroscience · 2025Article
- Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Dilated multi-scale residual attention (DMRA) U-Net: three-dimensional (3D) dilated multi-scale residual attention U-Net for brain tumor segmentation.Quantitative imaging in medicine and surgery · 2024Article
- Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction.Scientific reports · 2024Article
- Brain tumor segmentation in multimodal MRIFrontiers in neuroscience · 2022Article
- BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) · 2021Article
- Overview of Multi-Modal Brain Tumor MR Image Segmentation.Healthcare (Basel, Switzerland) · 2021Review
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2 authors.
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Abstract
Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. Variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorff distance (95%) of 4.953 , 6.299, 23.608 for the whole tumor, tumor core, and enhancing tumor, respectively. The corresponding results on the BraTS 2020 testing dataset are 0.8729, 0.8357, and 0.8205 for Dice score, and 11.4288, 19.9690, and 15.6711 for Hausdorff distance. The code is publicly available at https://github.com/shu-hai/two-stage-VAE-Attention-gate-BraTS2020.
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