Cancer image classification based on DenseNet model

Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination. Image processing introduced a promising strategy to facilitate disease classification and detection while diminishing unnecessary expenses.In this paper, we propose a novel metastatic cancer image classification model based on DenseNet Block, which can effectively identify metastatic cancer in small image patches taken from larger digital pathology scans. We evaluate the proposed approach to the slightly modified version of the PatchCamelyon (PCam) benchmark dataset. The dataset is the slightly modified version of the PatchCamelyon (PCam) benchmark dataset provided by Kaggle competition, which packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task. The experiments indicated that our model outperformed other classical methods like Resnet34, Vgg19. Moreover, we also conducted data augmentation experiment and study the relationship between Batches processed and loss value during the training and validation process.

基于DenseNet模型的癌症图像分类

计算机辅助诊断建立了对基于医学图像的检查进行可靠评估的方法。图像处理引入了一种有前途的策略，以促进疾病分类和检测，同时减少不必要的费用。.. 在本文中，我们提出了一种基于DenseNet Block的新型转移性癌症图像分类模型，该模型可以有效地识别从较大的数字病理扫描中获取的小图像斑块中的转移性癌症。我们评估对PatchCamelyon（PCam）基准数据集的稍加修改版本的建议方法。该数据集是由Kaggle竞赛提供的PatchCamelyon（PCam）基准数据集的略微修改版本，该数据集将与临床相关的转移检测任务打包为直接的二进制图像分类任务。实验表明，我们的模型优于其他经典方法，如Resnet34，Vgg19。此外，我们还进行了数据扩充实验，并研究了训练和验证过程中处理的批次与损失价值之间的关系。 （阅读更多）