A Novel Framework for Multimodal Brain Tumor Detection With Scarce Labels

文献类型： 外文期刊

第一作者： Ge, Yanning

作者： Ge, Yanning;Xu, Li;Wang, Xiaoding;Wang, Xiaoding;Que, Youxiong;Que, Youxiong;Piran, Md. Jalil

作者机构：

关键词： Tumors; Magnetic resonance imaging; Feature extraction; Computed tomography; Accuracy; Brain modeling; Medical diagnostic imaging; Image recognition; Clustering algorithms; Convolutional neural networks; Brain tumor detection; multimodal image processing; contrastive learning; dual-branch network; adaptive weight mask

期刊名称：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS （ 影响因子：6.8； 五年影响因子：7.0 ）

ISSN： 2168-2194

年卷期： 2025 年 29 卷 8 期

页码：

收录情况： SCI

摘要： Brain tumor detection has advanced significantly with the development of deep learning technology. Although multimodal data, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), has potential advantages in diagnostics, most existing studies rely solely on a single modality. This is because common fusion methods may lead to the loss of critical information when attempting multimodal fusion. Therefore, effectively integrating multimodal data has become a significant challenge. Additionally, medical image analysis requires large amounts of annotated data, and labeling images is a resource-intensive task that demands experienced professionals to spend a considerable amount of time. To address these challenges, this paper introduces a new unsupervised learning framework named Double-SimCLR. This framework builds on the foundation of contrastive learning and features a dual-branch structure, enabling direct and simultaneous processing of MRI and CT images for multimodal feature fusion. Given the "weak feature" characteristics of CT images (e.g., low soft tissue contrast and low resolution), we incorporated adaptive weight masking technology to enhance CT feature extraction. Moreover, we introduced a multimodal attention mechanism, which ensures that the model focuses on salient information, thereby elevating the precision and robustness of brain tumor detection. Even without substantial labeled data, experimental results demonstrate that Double-SimCLR achieves 93.458% accuracy, 92.463% precision, and a 93.058% F1-score, outperforming state-of-the-art (SOTA) models by 2.871%, 2.643%, and 3.098%, respectively.

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