论文标题
在卷积神经网络中利用自适应色增强，以进行深层皮肤病变细分
Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation
论文作者
论文摘要
皮肤镜图像中皮肤病变的完全自动检测可以促进早期诊断和抑制恶性黑色素瘤和非黑色素瘤皮肤癌。尽管卷积神经网络是一个强大的解决方案，但它们受到注释的皮肤镜筛选图像的照明频谱的限制，其中颜色是重要的歧视性特征。在本文中，我们提出了一种自适应颜色增强技术，以扩大数据表达和模型性能，同时调节色彩差异和饱和，以最大程度地减少使用合成数据的风险。通过深层可视化，我们定性地识别和验证网络学到的语义结构特征，用于将皮肤病变与正常皮肤组织区分开。整个系统的骰子比为0.891，敏感性为0.943，对ISIC 2018测试集进行分割的特异性为0.932。
Fully automatic detection of skin lesions in dermatoscopic images can facilitate early diagnosis and repression of malignant melanoma and non-melanoma skin cancer. Although convolutional neural networks are a powerful solution, they are limited by the illumination spectrum of annotated dermatoscopic screening images, where color is an important discriminative feature. In this paper, we propose an adaptive color augmentation technique to amplify data expression and model performance, while regulating color difference and saturation to minimize the risks of using synthetic data. Through deep visualization, we qualitatively identify and verify the semantic structural features learned by the network for discriminating skin lesions against normal skin tissue. The overall system achieves a Dice Ratio of 0.891 with 0.943 sensitivity and 0.932 specificity on the ISIC 2018 Testing Set for segmentation.
