基于注意力机制的深度学习模型，精准识别结直肠癌病理图像
MSRANetV2: An Explainable Deep Learning Architecture for Multi-class Classification of Colorectal Histopathological Images
- 融合残差注意力与塞挤压激励块，提取多尺度病理特征
- 在7K和100K数据集上准确率超99%，AUC接近1.0
- 结合Grad-CAM可视化，医生可理解模型决策依据
结直肠癌（CRC）是全球癌症死亡的主要原因之一，早期精准检测对改善患者预后至关重要。传统诊断方法如结肠镜和组织学检查存在主观性强、耗时长、变异大等问题。数字病理的发展使深度学习成为提升诊断精度与效率的重要手段。本文提出一种名为MSRANetV2的卷积神经网络架构，专为结直肠组织图像多类分类优化。模型以ResNet50V2为骨干，引入残差注意力机制与塞挤压-激励（SE）模块，有效提取深层语义与细粒度空间特征。通过通道对齐与上采样操作，融合多尺度表示，增强分类鲁棒性。在两个公开数据集CRC-VAL-HE-7K与NCT-CRC-HE-100K上采用五折分层交叉验证评估，结果表明：在7K数据集上，平均精确率、召回率、F1分数、AUC与测试准确率分别为0.9884±0.0151、0.9900±0.0151、0.9900±0.0145、0.9999±0.00006、0.9905±0.0025；在100K数据集上分别为0.9904±0.0091、0.9900±0.0071、0.9900±0.0071、0.9997±0.00016、0.9902±0.0006。同时，通过Grad-CAM可视化增强模型可解释性，突出医学相关组织区域。结果验证了MSRANetV2在结直肠癌组织分类中具备高可靠性、可解释性与高性能。
原文摘要 · Abstract (English)
Colorectal cancer (CRC) is a leading worldwide cause of cancer-related mortality, and the role of prompt precise detection is of paramount interest in improving patient outcomes. Conventional diagnostic methods such as colonoscopy and histological examination routinely exhibit subjectivity, are extremely time-consuming, and are susceptible to variation. Through the development of digital pathology, deep learning algorithms have become a powerful approach in enhancing diagnostic precision and efficiency. In our work, we proposed a convolutional neural network architecture named MSRANetV2, specially optimized for the classification of colorectal tissue images. The model employs a ResNet50V2 backbone, extended with residual attention mechanisms and squeeze-and-excitation (SE) blocks, to extract deep semantic and fine-grained spatial features. With channel alignment and upsampling operations, MSRANetV2 effectively fuses multi-scale representations, thereby enhancing the robustness of the classification. We evaluated our model on a five-fold stratified cross-validation strategy on two publicly available datasets: CRC-VAL-HE-7K and NCT-CRC-HE-100K. The proposed model achieved remarkable average Precision, recall, F1-score, AUC, and test accuracy were 0.9884 plus-minus 0.0151, 0.9900 plus-minus 0.0151, 0.9900 plus-minus 0.0145, 0.9999 plus-minus 0.00006, and 0.9905 plus-minus 0.0025 on the 7K dataset. On the 100K dataset, they were 0.9904 plus-minus 0.0091, 0.9900 plus-minus 0.0071, 0.9900 plus-minus 0.0071, 0.9997 plus-minus 0.00016, and 0.9902 plus-minus 0.0006. Additionally, Grad-CAM visualizations were incorporated to enhance model interpretability by highlighting tissue areas that are medically relevant. These findings validate that MSRANetV2 is a reliable, interpretable, and high-performing architectural model for classifying CRC tissues.
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