Harangi Balazs, Baran Agnes, Hajdu Andras
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2575-2578. doi: 10.1109/EMBC.2018.8512800.
Skin cancer is among the deadliest variants of cancer if not recognized and treated in time. This work focuses on the identification of this disease using an ensemble of state-of-the-art deep learning approaches. More specifically, we propose the aggregation of robust convolutional neural networks (CNNs) into one neural net architecture, where the final classification is achieved based on the weighted output of the member CNNs. Since our framework is realized within a single neural net architecture, all the parameters of the member CNNs and the weights applied in the fusion can be determined by backpropagation routinely applied for such tasks. The presented ensemble consists of the CNNs AlexNet, VGGNet, GoogLeNet, all of which have been won in subsequent years the most prominent worldwide image classification challenge ImageNet. For an objective evaluation of our approach, we have tested its performance on the official test database of the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection dedicated to skin cancer recognition. Our experimental studies show that the proposed approach is competitive in this field. Moreover, the ensemble-based approach outperformed all of its member CNNs.
皮肤癌如果不及时发现和治疗，是最致命的癌症变体之一。这项工作专注于使用一组最先进的深度学习方法来识别这种疾病。更具体地说，我们提出将强大的卷积神经网络（CNN）聚合到一个神经网络架构中，其中最终分类是基于成员CNN的加权输出实现的。由于我们的框架是在单个神经网络架构中实现的，成员CNN的所有参数以及融合中应用的权重都可以通过常规用于此类任务的反向传播来确定。所提出的集成由CNN AlexNet、VGGNet、GoogLeNet组成，所有这些网络在随后几年都赢得了全球最著名的图像分类挑战赛ImageNet。为了对我们的方法进行客观评估，我们在2017年IEEE国际生物医学成像研讨会（ISBI）关于皮肤病变分析以检测黑色素瘤的官方测试数据库上测试了其性能，该挑战赛致力于皮肤癌识别。我们的实验研究表明，所提出的方法在该领域具有竞争力。此外，基于集成的方法优于其所有成员CNN。
