ArticleMultimedia tools and applications2022
A multi-sequences MRI deep framework study applied to glioma classfication.
Article in Multimedia tools and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 21 citations in OpenAlex.
- MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.Scientific reports · 2025Article
- Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.BMC medical imaging · 2025Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging.PloS one · 2024Article
- Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning.Journal of clinical medicine · 2023Review
- Assessment of brain cancer atlas maps with multimodal imaging features.Journal of translational medicine · 2023Review
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a common tool for medical experts to the diagnosis of glioma. A set of multi-sequences from an MRI is selected according to the severity of the pathology. Our proposed approach aims moreto create a computer-aided system that is capable of helping morethe expert diagnose the brain gliomas. moreWe propose a supervised learning regime based on a convolutional neural network based framework and transfer learning techniques. Our research morefocuses on the performance of different pre-trained deep learning models with respect to different MRI sequences. We highlight the best combinations of such model-MRI sequence couple for our specific task of classifying healthy brain against brain with glioma. moreWe also propose to visually analyze the extracted deep features for studying the existing relation of the MRI sequences and models. This interpretability analysis gives some hints for medical expert to understand the diagnosis made by the models. Our study is based on the well-known BraTS datasets including multi-sequence images and expert diagnosis.
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Registered trials
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