ArticleScientific reports2020
Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- Glioblastoma survival prediction through MRI and clinical data integration with transfer learning.International journal of computer assisted radiology and surgery · 2026Article
- Mamba-based brain tumor segmentation of incomplete multi-modal MR images.Quantitative imaging in medicine and surgery · 2026Article
- Interpretability and performance of a 3D C-vit model for accurate grading of pediatric brain tumors.Frontiers in oncology · 2026Article
- GlioSurvQNet: A DuelContextAttn DQN Framework for Brain Tumor Prognosis with Metaheuristic Optimization.Diagnostics (Basel, Switzerland) · 2025Article
- OVERALL SURVIVAL PREDICTION OF BRAIN TUMOR PATIENTS WITH MULTIMODAL MRI USING SWIN UNETR.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.Journal of imaging informatics in medicine · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma.Journal of translational medicine · 2025Article
- Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.Current medical imaging · 2025Review
- Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images.Scientific reports · 2024Article
- Development of brain tumor radiogenomic classification using GAN-based augmentation of MRI slices in the newly released gazi brains dataset.BMC medical informatics and decision making · 2024Article
- End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images.Journal of imaging informatics in medicine · 2024Article
- Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting.Bioengineering (Basel, Switzerland) · 2024Article
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
- A hybrid deep CNN model for brain tumor image multi-classification.BMC medical imaging · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.Behavioural neurology · 2024Article
- Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients.Cancers · 2023Article
- Machine learning-ready remote sensing data for Maya archaeology.Scientific data · 2023Article
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4 authors.
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Abstract
A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge.
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