论文标题
使用临床和皮肤图像数据集的用于皮肤癌分类和新类检测的暹罗神经网络
Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
论文作者
论文摘要
皮肤癌是世界上最常见的恶性肿瘤。自动化的皮肤癌检测将显着提高早期检测率并防止死亡。为了帮助实现这一目标，已经发布了许多数据集，可用于训练深度学习系统 - 这些数据集为分类带来了令人印象深刻的结果。但是，这仅适用于他们接受的课程，而他们无法识别以前看不见的课程中的皮肤病变，从而使其无助于临床使用。我们可以通过包括所有可能的皮肤病变来大大增加数据集，尽管这总是会忽略一些课程。取而代之的是，我们评估了暹罗神经网络（SNN），这不仅允许我们对皮肤病变的图像进行分类，而且还允许我们识别与受过训练的类别不同的图像 - 使我们能够确定图像不是训练类的示例。我们评估皮肤病变皮肤镜和临床图像的SNN。我们在临床和皮肤镜数据集上分别获得了74.33％和85.61％的TOP-1分类精度水平。尽管这略低于最先进的结果，但SNN方法的优势是它可以检测到课外示例。我们的结果突出了SNN方法的潜力以及通往未来临床部署的途径。
Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim, a number of datasets have been released which can be used to train Deep Learning systems - these have produced impressive results for classification. However, this only works for the classes they are trained on whilst they are incapable of identifying skin lesions from previously unseen classes, making them unconducive for clinical use. We could look to massively increase the datasets by including all possible skin lesions, though this would always leave out some classes. Instead, we evaluate Siamese Neural Networks (SNNs), which not only allows us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes - allowing us to determine that an image is not an example of our training classes. We evaluate SNNs on both dermoscopic and clinical images of skin lesions. We obtain top-1 classification accuracy levels of 74.33% and 85.61% on clinical and dermoscopic datasets, respectively. Although this is slightly lower than the state-of-the-art results, the SNN approach has the advantage that it can detect out-of-class examples. Our results highlight the potential of an SNN approach as well as pathways towards future clinical deployment.
