论文标题
了解图像和输入分辨率对深数字病理补丁分类器的影响
Understanding the impact of image and input resolution on deep digital pathology patch classifiers
论文作者
论文摘要
我们考虑在数字病理学（DP）中有效学习，其中专家注释很昂贵，因此很少。我们探讨了图像和输入分辨率对DP补丁分类性能的影响。我们使用两个癌症斑块分类数据集PCAM和CRC来验证我们的研究结果。我们的实验表明，通过在注释范围和注释富含注释的环境中处理图像和输入分辨率可以通过操纵图像和输入分辨率来提高斑块分类性能。我们在两个数据集上显示了图像和输入分辨率和贴剂分类精度之间的正相关。通过利用图像和输入分辨率，与对PCAM数据集的原始图像分辨率中对100％数据训练的模型相比，我们对<1％数据进行训练的最终模型的性能同样出色。
We consider annotation efficient learning in Digital Pathology (DP), where expert annotations are expensive and thus scarce. We explore the impact of image and input resolution on DP patch classification performance. We use two cancer patch classification datasets PCam and CRC, to validate the results of our study. Our experiments show that patch classification performance can be improved by manipulating both the image and input resolution in annotation-scarce and annotation-rich environments. We show a positive correlation between the image and input resolution and the patch classification accuracy on both datasets. By exploiting the image and input resolution, our final model trained on < 1% of data performs equally well compared to the model trained on 100% of data in the original image resolution on the PCam dataset.
