TY - JOUR AU - Zhang, Gang AU - Yin, Jian AU - Li, Ziping AU - Su, Xiangyang AU - Li, Guozheng AU - Zhang, Honglai PY - 2013 DA - 2013/11/11 TI - Automated skin biopsy histopathological image annotation using multi-instance representation and learning JO - BMC Medical Genomics SP - S10 VL - 6 IS - 3 AB - 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. SN - 1755-8794 UR - https://doi.org/10.1186/1755-8794-6-S3-S10 DO - 10.1186/1755-8794-6-S3-S10 ID - Zhang2013 ER -