Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality, underscoring the need for improved diagnostic methods. This study seeks to enhance the classification accuracy of confocal laser endomicroscopy (pCLE) images for lung cancer by applying a dual transfer learning (TL) approach that incorporates histological imaging data. Methods: Histological samples and pCLE images, collected from 40 patients undergoing curative lung cancer surgeries, were selected to create 2 balanced datasets (800 benign and 800 malignant images each). Three CNN architectures—AlexNet, GoogLeNet, and ResNet—were pre-trained on ImageNet and re-trained on pCLE images (confocal TL) or using dual TL (first re-trained on histological images, then pCLE). Model performance was evaluated using accuracy and AUC across 50 independent runs with 10-fold cross-validation. Results: The dual TL approach statistically significant outperformed confocal TL, with AlexNet achieving a mean accuracy of 94.97% and an AUC of 0.98, surpassing GoogLeNet (91.43% accuracy, 0.97 AUC) and ResNet (89.87% accuracy, 0.96 AUC). All networks demonstrated statistically significant (p< 0.001) improvements in performance with dual TL. Additionally, dual TL models showed reductions in both false positives and false negatives, with class activation mappings highlighting enhanced focus on diagnostically relevant regions. Conclusions: Dual TL, integrating histological and pCLE imaging, results in a statistically significant improvement in lung cancer classification. This approach offers a promising framework for enhanced tissue classification. and with future development and testing, iy has the potential to improve patient outcomes.
背景/目的：肺癌仍是癌症相关死亡的主要原因，凸显了改进诊断方法的必要性。本研究旨在通过应用结合组织学成像数据的双重迁移学习（TL）方法，提高共聚焦激光显微内镜（pCLE）图像对肺癌的分类准确性。方法：从40例接受根治性肺癌手术的患者中收集组织学样本和pCLE图像，构建2个平衡数据集（各包含800张良性和800张恶性图像）。采用三种CNN架构——AlexNet、GoogLeNet和ResNet，先在ImageNet上进行预训练，随后在pCLE图像上重新训练（共聚焦TL），或采用双重TL（先在组织学图像上重新训练，再在pCLE图像上训练）。通过50次独立运行的10折交叉验证，使用准确率和AUC评估模型性能。结果：双重TL方法在统计学上显著优于共聚焦TL，其中AlexNet的平均准确率达到94.97%，AUC为0.98，优于GoogLeNet（准确率91.43%，AUC 0.97）和ResNet（准确率89.87%，AUC 0.96）。所有网络在采用双重TL后均表现出统计学上显著（p<0.001）的性能提升。此外，双重TL模型在假阳性和假阴性方面均有所减少，类别激活映射图显示模型对诊断相关区域的关注度增强。结论：结合组织学与pCLE成像的双重TL方法，在肺癌分类中实现了统计学上显著的性能提升。该方法为增强组织分类提供了一个有前景的框架，随着未来的开发与测试，有望改善患者的临床结局。
肿瘤(癌症)患者之家