论文标题
黑色素瘤分类的补丁选择
Patch Selection for Melanoma Classification
论文作者
论文摘要
在医学图像处理中，最重要的信息通常位于图像的小部分上。基于补丁的方法旨在仅使用图像中最相关的部分。寻找自动选择补丁的方法是一个挑战。在本文中，我们研究了两个选择斑块的标准：熵和光谱相似性标准。我们在不同级别的斑块大小上执行实验。我们在补丁的子集上训练卷积神经网络并分析训练时间。我们发现，除了需要减少预处理时间之外，根据熵收敛选择的贴片数据集训练的分类器要比基于光谱相似性标准所选择的分类器更快，此外，还可以提高精度更高。此外，与低熵斑块相比，高熵的斑块可导致更快的收敛性和更好的准确性。
In medical image processing, the most important information is often located on small parts of the image. Patch-based approaches aim at using only the most relevant parts of the image. Finding ways to automatically select the patches is a challenge. In this paper, we investigate two criteria to choose patches: entropy and a spectral similarity criterion. We perform experiments at different levels of patch size. We train a Convolutional Neural Network on the subsets of patches and analyze the training time. We find that, in addition to requiring less preprocessing time, the classifiers trained on the datasets of patches selected based on entropy converge faster than on those selected based on the spectral similarity criterion and, furthermore, lead to higher accuracy. Moreover, patches of high entropy lead to faster convergence and better accuracy than patches of low entropy.
