发表机构
American University of Beirut（贝鲁特美国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建两阶段深度学习框架，整合6种分类模型与4种分割模型，在病理数据集上测试后将最优模型整合，为自动化病理图像分析提供高效基准。
AI 中文摘要
肺癌仍是全球癌症相关死亡的主要原因，而组织病理学诊断常受观察者间差异及手动切片检查的繁重工作量影响。尽管深度学习在计算病理学领域展现出巨大潜力，但将组织分类与区域分割整合至统一分析框架的综合基准测试仍较为有限。本研究提出一种用于多类组织分类和像素级组织病理区域分割的两阶段深度学习框架，并对各阶段的最新架构进行系统比较。在组织分类阶段，研究人员在由LC25000和LungHist700合并而成的39000张图像组成的数据集上评估了6种模型：自定义卷积神经网络、VGG16、DenseNet、MobileNetV3、自定义视觉Transformer及YOLO11，这些模型需区分肺腺癌、肺鳞状细胞癌及正常肺组织。YOLO11取得最佳分类性能，准确率达98.38%，五折交叉验证准确率为98.21±0.35%，宏F1值为0.98。在区域分割阶段，研究人员采用GlaS腺体分割基准测试评估了U-Net、ResNet编码器U-Net、DeepLabV3+及YOLO11-seg，其中DeepLabV3+的交并比（Intersection over Union）达0.80，戴斯系数（Dice score）达0.89；而YOLO11-seg仅用约14倍更少的参数，即可达到与前者相当的0.79交并比。研究人员随后将分类与分割阶段表现最佳的模型整合为端到端框架，为自动化组织病理图像分析提供了准确、计算高效且可复现的基准。
英文摘要
Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination. Although deep learning has shown considerable potential in computational pathology, comprehensive benchmarks that integrate tissue classification and region segmentation within a unified analytical framework remain limited. This study presents a two-stage deep learning framework for multi-class tissue classification and pixel-level histopathological region segmentation, accompanied by a systematic comparison of state-of-the-art architectures at each stage. For tissue classification, six models, a custom convolutional neural network, VGG16, DenseNet, MobileNetV3, a custom Vision Transformer, and YOLO11, are evaluated on a combined dataset of 39,000 images derived from LC25000 and LungHist700. The models distinguish between adenocarcinoma, squamous cell carcinoma, and normal lung tissue. YOLO11 achieves the best classification performance, with an accuracy of 98.38%, a five-fold cross-validation accuracy of 98.21 +/- 0.35%, and a macro F1-score of 0.98. For region segmentation, U-Net, ResNet-encoder U-Net, DeepLabV3+, and YOLO11-seg are evaluated using the GlaS gland segmentation benchmark. DeepLabV3+ obtains the highest Intersection over Union of 0.80 and a Dice score of 0.89, while YOLO11-seg achieves a comparable Intersection over Union of 0.79 using approximately 14x fewer parameters. The best-performing classification and segmentation models are subsequently integrated into an end-to-end framework, providing an accurate, computationally efficient, and reproducible baseline for automated histopathological image analysis.
Comments8 pages
