title: Automated skin biopsy histopathological image annotation using multi-instance representation and learning.
abstract: With digitisation and the development of computer-aided diagnosis, histopathological image analysis has attracted considerable interest in recent years. In this article, we address the problem of the automated annotation of skin biopsy images, a special type of histopathological image analysis. In contrast to previous well-studied methods in histopathology, we propose a novel annotation method based on a multi-instance learning framework. The proposed framework first represents each skin biopsy image as a multi-instance sample using a graph cutting method, decomposing the image to a set of visually disjoint regions. Then, we construct two classification models using multi-instance learning algorithms, among which one provides determinate results and the other calculates a posterior probability. We evaluate the proposed annotation framework using a real dataset containing 6691 skin biopsy images, with 15 properties as target annotation terms. The results indicate that the proposed method is effective and medically acceptable.
authors.forename: Gang
authors.initials: G
authors.lastname: Zhang
authors.forename: Jian
authors.initials: J
authors.lastname: Yin
authors.forename: Ziping
authors.initials: Z
authors.lastname: Li
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Su
authors.forename: Guozheng
authors.initials: G
authors.lastname: Li
authors.forename: Honglai
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001706:Biopsy; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D010338:Pathology, Clinical / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000473:pathology*; D012871:Skin Diseases / Q000145:classification / Q000175:diagnosis / Q000473:pathology*