title: Augmenting multi-instance multilabel learning with sparse bayesian models for skin biopsy image analysis.
abstract: Skin biopsy images can reveal causes and severity of many skin diseases, which is a significant complement for skin surface inspection. Automatic annotation of skin biopsy image is an important problem for increasing efficiency and reducing the subjectiveness in diagnosis. However it is challenging particularly when there exists indirect relationship between annotation terms and local regions of a biopsy image, as well as local structures with different textures. In this paper, a novel method based on a recent proposed machine learning model, named multi-instance multilabel (MIML), is proposed to model the potential knowledge and experience of doctors on skin biopsy image annotation. We first show that the problem of skin biopsy image annotation can naturally be expressed as a MIML problem and then propose an image representation method that can capture both region structure and texture features, and a sparse Bayesian MIML algorithm which can produce probabilities indicating the confidence of annotation. The proposed algorithm framework is evaluated on a real clinical dataset containing 12,700 skin biopsy images. The results show that it is effective and prominent.
authors.affiliation: School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China ; School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Zhang
authors.affiliation: School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yin
authors.affiliation: Department of Dermatology and Venerology, The 3rd Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China.
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Su
authors.affiliation: The 2nd Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
authors.forename: Yongjing
authors.initials: Y
authors.lastname: Huang
authors.affiliation: The 2nd Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
authors.forename: Yingrong
authors.initials: Y
authors.lastname: Lao
authors.affiliation: The 2nd Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Liang
authors.affiliation: Department of Radiology, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, China.
authors.forename: Shanxing
authors.initials: S
authors.lastname: Ou
authors.affiliation: The 2nd Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
authors.forename: Honglai
authors.initials: H
authors.lastname: Zhang
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D001706:Biopsy / Q000379:methods; D046169:Dermoscopy / Q000379:methods*; D004282:Documentation / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000473:pathology; D012871:Skin Diseases / Q000145:classification / Q000473:pathology*; D013194:Staining and Labeling / Q000379:methods