论文标题
对角耳垂折痕检测的深度学习
Deep Learning for Diagonal Earlobe Crease Detection
论文作者
论文摘要
2022年6月，今天在医疗新闻上发表的一篇文章在其标题中提出了一个基本问题：耳垂折痕可以预测心脏病发作吗？作者解释说，末端动脉供应心脏和耳朵。换句话说，如果它们失去血液供应，则没有其他动脉可以接管，从而造成组织损害。因此，有些耳垂具有类似于皱纹的对角线折痕，线或深褶皱。在本文中，我们朝着检测这种特定标记的一步迈出了一步，通常称为Delc或Frank的标志。因此，我们将第一个DELC数据集提供给了公众。此外，我们还研究了在带注释的照片上的众多尖端骨干的性能。在实验上，我们证明可以通过将预训练的编码器与定制分类器相结合以达到97.7％的精度来解决这一挑战。此外，我们已经分析了性能和尺寸之间的主链权衡，将Mobilenet估计为最有前途的编码器。
An article published on Medical News Today in June 2022 presented a fundamental question in its title: Can an earlobe crease predict heart attacks? The author explained that end arteries supply the heart and ears. In other words, if they lose blood supply, no other arteries can take over, resulting in tissue damage. Consequently, some earlobes have a diagonal crease, line, or deep fold that resembles a wrinkle. In this paper, we take a step toward detecting this specific marker, commonly known as DELC or Frank's Sign. For this reason, we have made the first DELC dataset available to the public. In addition, we have investigated the performance of numerous cutting-edge backbones on annotated photos. Experimentally, we demonstrate that it is possible to solve this challenge by combining pre-trained encoders with a customized classifier to achieve 97.7% accuracy. Moreover, we have analyzed the backbone trade-off between performance and size, estimating MobileNet as the most promising encoder.
