Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis
Joint Authors
Lao, Yingrong
Liang, Zhaohui
Huang, Yongjing
Zhang, Gang
Yin, Jian
Ou, Shanxing
Su, Xiangyang
Zhang, Honglai
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-07
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Gang& Yin, Jian& Su, Xiangyang& Huang, Yongjing& Lao, Yingrong& Liang, Zhaohui…[et al.]. 2014. Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis. BioMed Research International،Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-461968
Modern Language Association (MLA)
Zhang, Gang…[et al.]. Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis. BioMed Research International No. 2014 (2014), pp.1-13.
https://search.emarefa.net/detail/BIM-461968
American Medical Association (AMA)
Zhang, Gang& Yin, Jian& Su, Xiangyang& Huang, Yongjing& Lao, Yingrong& Liang, Zhaohui…[et al.]. Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-461968
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-461968