title: A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides.
abstract: 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.
authors.affiliation: BioIngenium Research Group, Universidad Nacional de Colombia, Bogota, Colombia.
authors.forename: Angel
authors.initials: A
authors.lastname: Cruz-Roa
authors.forename: Fabio
authors.initials: F
authors.lastname: González
authors.forename: Joseph
authors.initials: J
authors.lastname: Galaro
authors.forename: Alexander R
authors.initials: AR
authors.lastname: Judkins
authors.forename: David
authors.initials: D
authors.lastname: Ellison
authors.forename: Jennifer
authors.initials: J
authors.lastname: Baccon
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
authors.forename: Eduardo
authors.initials: E
authors.lastname: Romero
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D002528:Cerebellar Neoplasms / Q000175:diagnosis* / Q000473:pathology; D016208:Databases, Factual; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008527:Medulloblastoma / Q000175:diagnosis* / Q000473:pathology; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D011336:Probability; D015203:Reproducibility of Results; D012984:Software