使用跨模态迁移学习与3D-CNN中的Grad-CAM进行MRI分析检测II型局灶性皮质发育不良
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis
AI总结：
本研究利用3D-CNN结合跨模态迁移学习和Grad-CAM，在170名受试者的MRI数据上检测II型局灶性皮质发育不良，最高分类准确率达80.3%，并通过Heat-Score指标提升了模型可解释性与癫痫灶定位能力。
摘要：
II型局灶性皮质发育不良（FCD）是药物难治性癫痫的主要原因，通常只能通过手术治疗。尽管其临床意义重大，但由于MRI中的细微异常，FCD的诊断非常困难，常导致误诊。本研究探讨了使用3D卷积神经网络（3D-CNN）进行FCD检测，数据集包含170名受试者（85名FCD患者和85名对照），由T1加权和FLAIR MRI扫描组成。特别地，本研究考察了跨模态迁移学习和可解释人工智能（XAI）技术所带来的益处，尤其是梯度加权类激活映射（Grad-CAM）。实现了ResNet架构（ResNet-18、-34和-50），采用迁移学习策略，使用来自分割任务的预训练权重。结果表明，迁移学习显著提高了分类准确率（最高达80.3%）和可解释性，后者通过一种新的Heat-Score指标衡量，该指标评估模型对临床相关区域的关注程度。Heat-Score指标的提升凸显了模型对癫痫发作区定位的能力，使AI预测与临床见解更加接近。这些结果强调了迁移学习（包括跨模态迁移学习）和XAI在推进基于AI的医学诊断中的重要性，尤其是对于FCD等难以诊断的病理。
英文摘要：
Focal cortical dysplasia (FCD) type II is a major cause of drug-resistant epilepsy, often curable only by surgery. Despite its clinical importance, the diagnosis of FCD is very difficult in MRI because of subtle abnormalities, leading to misdiagnosis. This study investigates the use of 3D convolutional neural networks (3D-CNNs) for FCD detection, using a dataset of 170 subjects (85 FCD patients and 85 controls) composed of T1-weighted and FLAIR MRI scans. In particular, it investigates the benefits obtained from cross-modality transfer learning and explainable artificial intelligence (XAI) techniques, in particular Gradient-weighted Class Activation Mapping (Grad-CAM). ResNet architectures (ResNet-18, -34, and -50) were implemented, employing transfer learning strategies that used pre-trained weights from segmentation tasks. Results indicate that transfer learning significantly enhances classification accuracy (up to 80.3%) and interpretability, as measured by a novel Heat-Score metric, which evaluates the model's focus on clinically relevant regions. Improvements in the Heat-Score metric underscore the model's seizure zone localization capabilities, bringing AI predictions and clinical insights closer together. These results highlight the importance of transfer learning, including cross-modality, and XAI in advancing AI-based medical diagnostics, especially for difficult-to-diagnose pathologies such as FCD.
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。
