ZooME: Efficient Melanoma Detection Using Zoom-in Attention and Metadata Embedding Deep Neural Network

Xing，Xiaoyan1,2; Song，Pingping1; Zhang，Kai1; Yang，Fang3,4,5; Dong，Yuhan1,2

10.1109/EMBC46164.2021.9630452

2021

1557-170X

978-1-7281-1180-3

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

4041-4044

1-5 Nov. 2021

Mexico

Melanoma detection is a crucial yet hard task for both dermatologists and computer-aided diagnosis (CAD). Many traditional machine learning algorithms including deep learning-based methods are employed for melanoma classification. However, more and more complex network architectures do not harvest a leap in model performance. In this paper, we aim to enhance the credibility of CAD approach for melanoma by paying more attention to clinically important information. We propose a Zoom-in Attention and Metadata Embedding (ZooME) melanoma detection network by: 1) introducing a Zoom-in Attention model to better extract and utilize unique pathological information of dermoscopy images; 2) embedding patients' demographic information including age, gender, and anatomic body site, to provide well-rounded information for better prediction. We apply a ten-fold cross-validation on the latest ISIC-2020 dataset with 33,126 dermoscopy images. The proposed ZooME achieved state-of-the-art results with 92.23% in AUC score, 84.59% in accuracy, 85.95% in sensitivity, and 84.63% in specialty, respectively.

This work establishes an efficient melanoma detection method with pathological and demographic information in consideration.

通讯

英语

EI

20220811669973

2-s2.0-85122520615

Scopus

1.Shenzhen Internation Gratuate School,Tsinghua University,ShenZhen,China
2.China International Exchange and Promotive Association for Medical and Health Care,Beijing,China
3.Department of Dermatology,Shenzhen People's Hospital,The Second Clinical Medical College,Jinan University,Shenzhen,Guangdong,China
4.The First Affiliated Hospital,Southern University of Science and Technology,Shenzhen,Guangdong,China
5.Candidate Branch of National Clinical Research Center for Skin Diseases,

Xing，Xiaoyan,Song，Pingping,Zhang，Kai,et al. ZooME: Efficient Melanoma Detection Using Zoom-in Attention and Metadata Embedding Deep Neural Network[C],2021:4041-4044.