ArticleFrontiers in oncology2021
Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Multi-attention Mechanism for Enhanced Pseudo-3D Prostate Zonal Segmentation.Journal of imaging informatics in medicine · 2025Article
- Optimal Res-UNET architecture with deep supervision for tumor segmentation.Frontiers in medicine · 2025Article
- Brain tumor image segmentation method using hybrid attention module and improved mask RCNN.Scientific reports · 2024Article
- Few-Shot Classification with Multiscale Feature Fusion for Clinical Skin Disease Diagnosis.Clinical, cosmetic and investigational dermatology · 2024Article
- Automatic segmentation of nasopharyngeal carcinoma on CT images using efficient UNet-2.5D ensemble with semi-supervised pretext task pretraining.Frontiers in oncology · 2022Article
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3 authors.
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Abstract
Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, simple deep learning models are difficult to locate the tumor area and obtain accurate segmentation boundaries. In order to solve the problems above, we propose a 2D end-to-end model of attention R2U-Net with multi-task deep supervision (MTDS). MTDS can extract rich semantic information from images, obtain accurate segmentation boundaries, and prevent overfitting problems in deep learning. Furthermore, we propose the attention pre-activation residual module (APR), which is an attention mechanism based on multi-scale fusion methods. APR is suitable for a deep learning model to help the network locate the tumor area accurately. Finally, we evaluate our proposed model on the public BraTS 2020 validation dataset which consists of 125 cases, and got a competitive brain tumor segmentation result. Compared with the state-of-the-art brain tumor segmentation methods, our method has the characteristics of a small parameter and low computational cost.
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