ArticleFrontiers in oncology2021
A Deep Multi-Task Learning Framework 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 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- GLAPAL-H: Global, Local, and Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.IEEE transactions on bio-medical engineering · 2026Article
- Localization-guided segmentation of Gliomas via 3D ReFusionNet.Frontiers in oncology · 2026Article
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.medRxiv : the preprint server for health sciences · 2025Article
- Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images.PloS one · 2025Article
- End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images.Journal of imaging informatics in medicine · 2024Article
- Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework.Scientific reports · 2024Article
- Multilevel chitosan-gelatin particles loaded with P4HA1 siRNA suppress glioma development.Drug delivery and translational research · 2024Article
- Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.Annals of medicine and surgery (2012) · 2024Article
- Large-Kernel Attention for 3D Medical Image Segmentation.Cognitive computation · 2024Article
- G-Net: Implementing an enhanced brain tumor segmentation framework using semantic segmentation design.PloS one · 2024Article
- Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals.Sensors (Basel, Switzerland) · 2023Article
- An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble.Sensors (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.Diagnostics (Basel, Switzerland) · 2023Review
- A lightweight hierarchical convolution network for brain tumor segmentation.BMC bioinformatics · 2022Article
- Insights about circadian clock in glioma: From molecular pathways to therapeutic drugs.CNS neuroscience & therapeutics · 2022Review
- CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.Frontiers in oncology · 2022Article
- AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models.Journal of biotechnology and biomedicine · 2022Article
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7 authors.
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Abstract
Glioma is the most common primary central nervous system tumor, accounting for about half of all intracranial primary tumors. As a non-invasive examination method, MRI has an extremely important guiding role in the clinical intervention of tumors. However, manually segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects the implementation of follow-up diagnosis and treatment plans. With the development of deep learning, medical image segmentation is gradually automated. However, brain tumors are easily confused with strokes and serious imbalances between classes make brain tumor segmentation one of the most difficult tasks in MRI segmentation. In order to solve these problems, we propose a deep multi-task learning framework and integrate a multi-depth fusion module in the framework to accurately segment brain tumors. In this framework, we have added a distance transform decoder based on the V-Net, which can make the segmentation contour generated by the mask decoder more accurate and reduce the generation of rough boundaries. In order to combine the different tasks of the two decoders, we weighted and added their corresponding loss functions, where the distance map prediction regularized the mask prediction. At the same time, the multi-depth fusion module in the encoder can enhance the ability of the network to extract features. The accuracy of the model will be evaluated online using the multispectral MRI records of the BraTS 2018, BraTS 2019, and BraTS 2020 datasets. This method obtains high-quality segmentation results, and the average Dice is as high as 78%. The experimental results show that this model has great potential in segmenting brain tumors automatically and accurately.
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