A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides.

TitleA visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides.
Publication TypeJournal Article
Year of Publication2012
AuthorsCruz-Roa, A, González F, Galaro J, Judkins AR, Ellison D, Baccon J, Madabhushi A, Romero E
JournalInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
IssuePt 1
Date Published2012
KeywordsAlgorithms, Artificial Intelligence, Automation, Cerebellar Neoplasms, Databases, Factual, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Medulloblastoma, Models, Statistical, Pattern Recognition, Automated, Probability, Reproducibility of Results, Software

A method for automatic analysis and interpretation of histopathology images is presented. The method uses a representation of the image data set based on bag of features histograms built from visual dictionary of Haar-based patches and a novel visual latent semantic strategy for characterizing the visual content of a set of images. One important contribution of the method is the provision of an interpretability layer, which is able to explain a particular classification by visually mapping the most important visual patterns associated with such classification. The method was evaluated on a challenging problem involving automated discrimination of medulloblastoma tumors based on image derived attributes from whole slide images as anaplastic or non-anaplastic. The data set comprised 10 labeled histopathological patient studies, 5 for anaplastic and 5 for non-anaplastic, where 750 square images cropped randomly from cancerous region from whole slide per study. The experimental results show that the new method is competitive in terms of classification accuracy achieving 0.87 in average.

PDF Link


Alternate JournalMed Image Comput Comput Assist Interv

 *IEEE COPYRIGHT NOTICE: 1997 IEEE. * Personal use of this material is permitted. However, permission to reprint/ republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

*COPYRIGHT NOTICE:* These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.