Paper Info
Title
Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks
Abstract
In this study, we investigate what a practically useful approach is in order to achieve robust skin disease diagnosis. A direct approach is to target the ground truth diagnosis labels, while an alternative approach instead focuses on determining skin lesion characteristics that are more visually consistent and discernible. We argue that, for computer aided skin disease diagnosis, it is both more realistic and more useful that lesion type tags should be considered as the target of an automated diagnosis system such that the system can first achieve a high accuracy in describing skin lesions, and in turn facilitate disease diagnosis using lesion characteristics in conjunction with other evidences. To further meet such an objective, we employ convolutional neutral networks (CNNs) for both the disease-targeted and lesion-targeted classifications. We have collected a large-scale and diverse dataset of 75,665 skin disease images from six publicly available dermatology atlantes. Then we train and compare both disease-targeted and lesion-targeted classifiers, respectively. For disease-targeted classification, only 27.6% top-1 accuracy and 57.9% top-5 accuracy are achieved with a mean average precision (mAP) of 0.42. In contrast, for lesion-targeted classification, we can achieve a much higher mAP of 0.70.
Year
DOI
Venue
2018
10.1109/ICPR.2016.7899659
2016 23rd International Conference on Pattern Recognition (ICPR)
Keywords
DocType
Volume
skin disease classification,skin lesion characterization,convolutional neural networks
Journal
abs/1812.03520
ISSN
ISBN
Citations 
1051-4651
978-1-5090-4848-9
1
PageRank 
References 
Authors
0.37
7
3
Name
Order
Citations
PageRank
Haofu Liao1276.97
Yuncheng Li222514.29
Jiebo Luo36314374.00