Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss.

Guo, Lei; Xie, Gang; Xu, Xinying; Ren, Jinchang

Abstract

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology.

Citation

GUO, L., XIE, G., XU, X. and REN, J. 2020. Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss. Sensors [online], 20(20), article 5786. Available from: https://doi.org/10.3390/s20205786

• Journal Article Type: Letter
• Acceptance Date: Oct 9, 2020
• Online Publication Date: Oct 13, 2020
• Publication Date: Oct 31, 2020
• Deposit Date: May 6, 2022
• Publicly Available Date: Jun 6, 2022
• Journal: Sensors
• Print ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 20
• Article Number: 5786
• DOI: https://doi.org/10.3390/s20205786
• Keywords: Melanoma recognition; Embedding loss; Covariance discriminant loss; Deep convolutional neural network; Dermoscopy image
• Public URL: https://rgu-repository.worktribe.com/output/1085450