用空间注意力跳跃连接提升脑肿瘤分类准确率
SKIPNet: Spatial Attention Skip Connections for Enhanced Brain Tumor Classification
- 引入空间注意力机制的跳跃连接，增强上下文信息聚合
- 在MRI数据上达到96.90%分类准确率，优于基线模型
- 适合需要高效精准脑肿瘤自动分析的临床场景
通过磁共振成像（MRI）早期检测脑肿瘤对及时治疗至关重要，但偏远地区仍缺乏诊断设施。胶质瘤是最常见的原发性脑肿瘤，起源于脑和脊髓的胶质细胞癌变，其中胶质母细胞瘤患者的中位生存期不足14个月。MRI是无创且有效的肿瘤检测手段，但手动分割脑部MRI图像对神经放射科医生而言仍是一项劳动密集型任务。近年来，计算机辅助设计（CAD）、机器学习（ML）和深度学习（DL）为自动化该过程提供了有前景的解决方案。本研究提出一种基于MRI数据的脑肿瘤自动检测与分类深度学习模型。该模型结合空间注意力机制，实现了96.90%的准确率，显著增强了上下文信息的聚合能力，提升了模式识别效果。实验结果表明，所提方法优于基线模型，展现出鲁棒性与在自动化MRI脑肿瘤分析中的应用潜力。
原文摘要 · Abstract (English)
Early detection of brain tumors through magnetic resonance imaging (MRI) is essential for timely treatment, yet access to diagnostic facilities remains limited in remote areas. Gliomas, the most common primary brain tumors, arise from the carcinogenesis of glial cells in the brain and spinal cord, with glioblastoma patients having a median survival time of less than 14 months. MRI serves as a non-invasive and effective method for tumor detection, but manual segmentation of brain MRI scans has traditionally been a labor-intensive task for neuroradiologists. Recent advancements in computer-aided design (CAD), machine learning (ML), and deep learning (DL) offer promising solutions for automating this process. This study proposes an automated deep learning model for brain tumor detection and classification using MRI data. The model, incorporating spatial attention, achieved 96.90% accuracy, enhancing the aggregation of contextual information for better pattern recognition. Experimental results demonstrate that the proposed approach outperforms baseline models, highlighting its robustness and potential for advancing automated MRI-based brain tumor analysis.
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