دورية أكاديمية
A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts ...
| العنوان: | A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts ... |
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| المؤلفون: | Zhang, Xinru, Ou, Ni, Basaran, Berke Doga, Visentin, Marco, Qiao, Mengyun, Gu, Renyang, Ouyang, Cheng, Liu, Yaou, Matthew, Paul M., Ye, Chuyang, Bai, Wenjia |
| بيانات النشر: | arXiv |
| سنة النشر: | 2024 |
| المجموعة: | DataCite Metadata Store (German National Library of Science and Technology) |
| مصطلحات موضوعية: | Image and Video Processing eess.IV, Computer Vision and Pattern Recognition cs.CV, FOS Electrical engineering, electronic engineering, information engineering, FOS Computer and information sciences |
| الوصف: | Brain lesion segmentation plays an essential role in neurological research and diagnosis. As brain lesions can be caused by various pathological alterations, different types of brain lesions tend to manifest with different characteristics on different imaging modalities. Due to this complexity, brain lesion segmentation methods are often developed in a task-specific manner. A specific segmentation model is developed for a particular lesion type and imaging modality. However, the use of task-specific models requires predetermination of the lesion type and imaging modality, which complicates their deployment in real-world scenarios. In this work, we propose a universal foundation model for 3D brain lesion segmentation, which can automatically segment different types of brain lesions for input data of various imaging modalities. We formulate a novel Mixture of Modality Experts (MoME) framework with multiple expert networks attending to different imaging modalities. A hierarchical gating network combines the ... : The work has been early accepted by MICCAI 2024 ... |
| نوع الوثيقة: | article in journal/newspaper report |
| اللغة: | unknown |
| DOI: | 10.48550/arxiv.2405.10246 |
| الإتاحة: | https://doi.org/10.48550/arxiv.2405.10246Test https://arxiv.org/abs/2405.10246Test |
| حقوق: | arXiv.org perpetual, non-exclusive license ; http://arxiv.org/licenses/nonexclusive-distrib/1.0Test/ |
| رقم الانضمام: | edsbas.C0107687 |
| قاعدة البيانات: | BASE |
| DOI: | 10.48550/arxiv.2405.10246 |
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