一个多任务深度学习框架，使用动态功能连接定位脑肿瘤患者的语言皮层
A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity
摘要：
我们提出了一种新的深度学习框架，该框架使用动态功能连接来同时定位脑肿瘤患者的语言和运动区。我们的方法利用卷积层从动态连接矩阵和长短期记忆（LSTM）注意力网络中提取基于图的特征，以在分类过程中对相关时间点进行加权。我们模型的最后阶段采用多任务学习来识别不同的雄辩子系统。我们独特的训练策略在感兴趣的认知网络之间找到了一个共享的表示，这使我们能够处理缺失的患者数据。我们评估我们的方法从56例脑肿瘤患者的静息状态fMRI数据，同时使用任务fMRI激活作为替代地面真理标签的培训和测试。我们的模型比传统的深度学习方法具有更高的定位精度，即使在左半球偏侧的情况下训练，也可以识别双侧语言区域。因此，我们的方法可能最终是有用的术前映射肿瘤患者。
英文摘要：
We present a novel deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex in brain tumor patients. Our method leverages convolutional layers to extract graph-based features from the dynamic connectivity matrices and a long-short term memory (LSTM) attention network to weight the relevant time points during classification. The final stage of our model employs multi-task learning to identify different eloquent subsystems. Our unique training strategy finds a shared representation between the cognitive networks of interest, which enables us to handle missing patient data. We evaluate our method on resting-state fMRI data from 56 brain tumor patients while using task fMRI activations as surrogate ground-truth labels for training and testing. Our model achieves higher localization accuracies than conventional deep learning approaches and can identify bilateral language areas even when trained on left-hemisphere lateralized cases. Hence, our method may ultimately be useful for preoperative mapping in tumor patients.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
