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首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification
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GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification

机译：GP-CNN-DTEL：全球部件CNN模型，具有数据转换的集体学习，用于皮肤病变分类

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摘要

Precise skin lesion classification is still challenging due to two problems, i.e., (1) inter-class similarity and intra-class variation of skin lesion images, and (2) the weak generalization ability of single Deep Convolutional Neural Network trained with limited data. Therefore, we propose a Global-Part Convolutional Neural Network (GP-CNN) model, which treats the fine-grained local information and global context information with equal importance. The Global-Part model consists of a Global Convolutional Neural Network (G-CNN) and a Part Convolutional Neural Network (P-CNN). Specifically, the G-CNN is trained with downscaled dermoscopy images, and is used to extract the global-scale information of dermoscopy images and produce the Classification Activation Map (CAM). While the P-CNN is trained with the CAM guided cropped image patches and is used to capture local-scale information of skin lesion regions. Additionally, we present a data-transformed ensemble learning strategy, which can further boost the classification performance by integrating the different discriminant information from GP-CNNs that are trained with original images, color constancy transformed images, and feature saliency transformed images, respectively. The proposed method is evaluated on the ISIC 2016 and ISIC 2017 Skin Lesion Challenge (SLC) classification datasets. Experimental results indicate that the proposed method can achieve the state-of-the-art skin lesion classification performance (i.e., an AP value of 0.718 on the ISIC 2016 SLC dataset and an Average Auc value of 0.926 on the ISIC 2017 SLC dataset) without any external data, compared with other current methods which need to use external data.
机译：由于两个问题，即（1）皮肤病变图像的阶级相似性和阶级的阶级相似性和阶级阶级变异，精确的皮肤病变分类仍然具有挑战性，并且（2）具有限制数据的单个深卷积神经网络的弱泛化能力。因此，我们提出了一个全球部件卷积神经网络（GP-CNN）模型，其处理细粒度的本地信息和全球背景信息，同等重要。全局零件模型包括全球卷积神经网络（G-CNN）和部分卷积神经网络（P-CNN）。具体地，G-CNN用较次级DerMicopy图像培训，用于提取DermoSicopy图像的全局级别信息并产生分类激活图（CAM）。虽然P-CNN用凸轮引导的裁剪图像贴片训练，并且用于捕获皮肤病变区的局部级信息。此外，我们介绍了一种数据变换的集合学习策略，其可以通过将不同的判别信息从由原始图像，颜色恒定变换的图像和特征显着变换图像分别集成来自GP-CNN的不同判别信息进一步提高分类性能。所提出的方法是在ISIC 2016和ISIC 2017皮肤病尼挑战（SLC）分类数据集上的评估。实验结果表明，该方法可以实现最先进的皮肤病变分类性能（即，在ISIC 2016 SLC数据集上的AP值为0.718，ISIC 2017 SLC数据集上的平均AUC值为0.926）与需要使用外部数据的其他当前方法相比，任何外部数据。

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