论文标题
基于深度学习方法的转移性癌症图像分类
Metastatic Cancer Image Classification Based On Deep Learning Method
论文作者
论文摘要
使用组织病理学图像自动对癌症进行分类是准确检测癌症的艰巨任务，尤其是在从较大的数字病理学扫描中获得的小图像斑块中鉴定转移性癌症。计算机诊断技术吸引了研究人员的广泛关注。在本文中，我们提出了一种Noval方法，该方法结合了图像分类，Densenet169框架和整流ADAM优化算法中的深度学习算法。 Densenet的连接模式是从任何一层到所有连续层的直接连接，它们可以有效地改善不同层之间的信息流。因为RADAM不容易属于本地最佳解决方案，并且它可以在模型训练中迅速收敛。实验结果表明，我们的模型比其他经典卷积神经网络的方法具有出色的性能，例如VGG19，Resnet34，Resnet50。特别是，我们的Densenet169模型的AUC-ROC得分比VGG19模型高1.77％，精度得分高1.50％。此外，我们还研究了在训练阶段和验证阶段处理的损失价值与批处理之间的关系，并获得了一些重要而有趣的发现。
Using histopathological images to automatically classify cancer is a difficult task for accurately detecting cancer, especially to identify metastatic cancer in small image patches obtained from larger digital pathology scans. Computer diagnosis technology has attracted wide attention from researchers. In this paper, we propose a noval method which combines the deep learning algorithm in image classification, the DenseNet169 framework and Rectified Adam optimization algorithm. The connectivity pattern of DenseNet is direct connections from any layer to all consecutive layers, which can effectively improve the information flow between different layers. With the fact that RAdam is not easy to fall into a local optimal solution, and it can converge quickly in model training. The experimental results shows that our model achieves superior performance over the other classical convolutional neural networks approaches, such as Vgg19, Resnet34, Resnet50. In particular, the Auc-Roc score of our DenseNet169 model is 1.77% higher than Vgg19 model, and the Accuracy score is 1.50% higher. Moreover, we also study the relationship between loss value and batches processed during the training stage and validation stage, and obtain some important and interesting findings.
