论文标题
语义细分的卷积神经网络：应用于生物医学图像的小数据集
Convolution Neural Networks for Semantic Segmentation: Application to Small Datasets of Biomedical Images
论文作者
论文摘要
本论文研究了卷积神经网络（CNN）产生的分割结果在应用于小型生物医学数据集时彼此不同。我们使用不同的体系结构，参数和超参数，试图找出更好的任务配置，并试图找出基本的规律性。两个工作数据集来自研究的生物医学领域。我们对两种类型的网络进行了许多实验，并且收到的结果表明了网络的某些实验和参数的偏好而不是其他实验。所有测试结果均在表中给出，并显示了一些选定的结果图，并显示了分割预测以更好地说明。
This thesis studies how the segmentation results, produced by convolutional neural networks (CNN), is different from each other when applied to small biomedical datasets. We use different architectures, parameters and hyper-parameters, trying to find out the better configurations for our task, and trying to find out underlying regularities. Two working datasets are from biomedical area of research. We conducted a lot of experiments with the two types of networks and the received results have shown the preference of some conditions of experiments and parameters of the networks over the others. All testing results are given in the tables and some selected resulting graphs and segmentation predictions are shown for better illustration.
