Fine-grained Prototype Network for MRI Sequence Classification

The proposed new MRI sequence classification model, SequencesNet, addresses the problem of subtle inter-class differences and significant intraclass variations existing in medical images. The modular design of SequencesNet can be extended to other medical imaging tasks, including but not limited to multimodal image fusion, lesion detection, and disease staging. Future work can be done to decrease the computational complexity and increase the generalization of the model.

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Publication Details

Journal
Current Medical Imaging Formerly Current Medical Imaging Reviews
Published
2025-08-04
DOI
https://doi.org/10.2174/0115734056361649250717162910
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
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article

Fine-grained Prototype Network for MRI Sequence Classification

C. Q. Yuan, Xibin Jia, Chuanxu Yang, Luo Wang
Current Medical Imaging Formerly Current Medical Imaging Reviews
Brain Tumor Detection and Classification
article

Fine-grained Prototype Network for MRI Sequence Classification

C. Q. Yuan, Xibin Jia, Chuanxu Yang, Luo Wang
article en

Abstract

The proposed new MRI sequence classification model, SequencesNet, addresses the problem of subtle inter-class differences and significant intraclass variations existing in medical images. The modular design of SequencesNet can be extended to other medical imaging tasks, including but not limited to multimodal image fusion, lesion detection, and disease staging. Future work can be done to decrease the computational complexity and increase the generalization of the model.

Current Medical Imaging Formerly Current Medical Imaging ReviewsVol. 21
Beijing University of Technology (CN)
Reduced inequalities, Peace, Justice and strong institutions
Openalex Percentile: Top 76%
Brain Tumor Detection and Classification
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