对比6种模型在肺癌病理图像分类与分割中的表现，选出高效高精度方案。
Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology
- 分两阶段用多种模型对比，统一框架评估分类与分割性能。
- YOLO11分类准确率达98.38%，DeepLabV3+分割IoU达0.80。
- 推荐使用轻量级YOLO11-seg实现高效精准的端到端分析。
肺癌是全球癌症致死首要原因，而病理诊断易受主观差异和人工阅片负担影响。尽管深度学习在计算病理学中展现潜力，但集成组织分类与区域分割的综合性基准仍较缺乏。本研究提出一个两阶段深度学习框架，用于多类组织分类与像素级区域分割，并系统比较各阶段先进架构。分类阶段在包含39,000张图像的联合数据集（LC25000与LungHist700）上评估六种模型：自定义CNN、VGG16、DenseNet、MobileNetV3、自定义Vision Transformer及YOLO11，区分腺癌、鳞癌与正常肺组织。YOLO11表现最佳，准确率98.38%，五折交叉验证准确率98.21±0.35%，宏F1分数0.98。分割阶段在GlaS腺体分割基准上测试U-Net、ResNet编码器U-Net、DeepLabV3+和YOLO11-seg，DeepLabV3+获最高交并比0.80与Dice分数0.89，而YOLO11-seg仅用约14倍更少参数即达0.79的交并比。最优分类与分割模型整合为端到端框架，提供准确、高效且可复现的自动化病理图像分析基线。
原文摘要 · Abstract (English)
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.
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