论文标题
少更多：样品选择和标签调理可以改善皮肤病变细分
Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation
论文作者
论文摘要
分割皮肤病变的图像与本身和协助病变分类相关，但在获得带注释的数据方面遇到了挑战。在这项工作中，我们表明，通过最佳的通道间一致性选择培训样本，并调节地面掩护以消除过多的细节，从而可以通过更少的数据来改善细分。考虑到几种变化来源，我们进行了详尽的实验设计，包括三个不同的测试集，两个不同的深度学习架构和几种复制，总共进行了540次实验运行。我们发现，通过选择更好的深度学习模型获得的样本选择和细节去除可能分别具有相应的影响，分别对相应的影响分别为相应的12％和16％。
Segmenting skin lesions images is relevant both for itself and for assisting in lesion classification, but suffers from the challenge in obtaining annotated data. In this work, we show that segmentation may improve with less data, by selecting the training samples with best inter-annotator agreement, and conditioning the ground-truth masks to remove excessive detail. We perform an exhaustive experimental design considering several sources of variation, including three different test sets, two different deep-learning architectures, and several replications, for a total of 540 experimental runs. We found that sample selection and detail removal may have impacts corresponding, respectively, to 12% and 16% of the one obtained by picking a better deep-learning model.
