[1]LIU Jiaxuan,HU Feiyi,ZHANG Hui,et al.Dermoscopic images classification based on context and instance-level feature of self-supervised learning[J].CAAI Transactions on Intelligent Systems,2023,18(4):783-792.[doi:10.11992/tis.202211010]
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Dermoscopic images classification based on context and instance-level feature of self-supervised learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 783-792 Column: 学术论文—智能系统 Public date: 2023-07-15
Title:
Dermoscopic images classification based on context and instance-level feature of self-supervised learning
Author(s):
LIU Jiaxuan1;  HU Feiyi2;  ZHANG Hui3;  ZHANG Jinzhou1;  LI Ling1
1. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China;
2. School of Electrical and Information Engineering, Hunan University, Changsha 410000, China;
3. School of Robotics, Hunan University, Changsha 410012, China
Keywords:
skin disease diagnosis; self-supervised learning; feature extraction; image classification; transfer learning; convolutional neural network; medical image processing; auxiliary diagnosis
CLC:
TP391
DOI:
10.11992/tis.202211010
Abstract:
The skin disease detection method based on deep learning is limited by the difficulty of annotations, high cost, time consuming and labor-intensive labeling of dermoscopy images. This paper proposes a self-supervised learning method for dermoscopy images, which uses unmarked dermoscopy images to improve the classification accuracy of skin diseases. According to the characteristics of high similarity and single information of the spatial structure of dermoscopic images, a rotation prediction pretext task is designed to predict the rotation angle of the image through the model, so as to constrain the model’s attention to pathological areas of the image, and learn the context feature. At the same time, the paper presents an instance discrimination task and comparative learning of positive and negative instances to obtain the instance information of dermatoscope images, which provides guidance for global classification information in the classification of dermatoscope images. The initial skin disease detection model is obtained by fusing context features and instance semantic information. The experimental results show that the self-supervised learning method proposed in this paper can mine important information from unmarked dermoscopy samples, pay more attention to the lesion area of dermoscopy images, and make the classification results more accurate by fusing the characteristic information of dermoscopy images.
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