论文标题
通过中心为导向的边缘自由行细胞损失，高度不平衡的数据集中的皮肤病变检测的深度聚类
Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
论文作者
论文摘要
黑色素瘤是一种致命的皮肤癌，可以在早期诊断出生存率大幅度提高。基于学习的方法对从皮肤镜图像检测黑色素瘤检测有很大的希望。但是，由于黑色素瘤是一种罕见的疾病，因此皮肤病变的现有数据库主要包含高度不平衡的良性样本数量与恶性样本。反过来，由于多数类的统计优势，这种不平衡在分类模型中引起了实质性偏见。为了解决这个问题，我们基于皮肤镜图像的潜在空间嵌入了一种深层聚类方法。使用一种新型的面向中心的边缘三重损失（com-triplet）实现了聚类，从卷积神经网络骨架上执行的图像嵌入中实现了聚类。所提出的方法旨在形成最大分离的聚类中心，而不是最大程度地减少分类误差，因此对类别不平衡的敏感性较小。为了避免需要标记的数据，我们进一步建议基于高斯混合模型生成的伪标记实现com-triplet。全面的实验表明，与三重态损失的com-triplet损失的深度聚类优于三胞胎损失，以及在受监督和无监督的环境中竞争的分类器。
Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for the detection of melanoma from dermoscopic images. However, since melanoma is a rare disease, existing databases of skin lesions predominantly contain highly imbalanced numbers of benign versus malignant samples. In turn, this imbalance introduces substantial bias in classification models due to the statistical dominance of the majority class. To address this issue, we introduce a deep clustering approach based on the latent-space embedding of dermoscopic images. Clustering is achieved using a novel center-oriented margin-free triplet loss (COM-Triplet) enforced on image embeddings from a convolutional neural network backbone. The proposed method aims to form maximally-separated cluster centers as opposed to minimizing classification error, so it is less sensitive to class imbalance. To avoid the need for labeled data, we further propose to implement COM-Triplet based on pseudo-labels generated by a Gaussian mixture model. Comprehensive experiments show that deep clustering with COM-Triplet loss outperforms clustering with triplet loss, and competing classifiers in both supervised and unsupervised settings.
