In this paper we compare a variety of unsupervised probabilistic models used to represent a data set consisting of textual and image information. We show that those based on latent Dirichlet allocation (LDA) out perform traditional mixture models in likelihood comparison. The data set is taken from radiology; a combination of medical images and consultants reports. The task of learning to classify individual tissue, or disease types, requires expert hand labeled data. This is both: expensive to produce and prone to inconsistencies in labeling. Here we present methods that require no hand labeling and also automatically discover sub-types of disease. The learnt models can be used for both prediction and classification of new unseen data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Unsupervised learning in radiology using novel latent variable models


    Beteiligte:
    Carrivick, L. (Autor:in) / Prabhu, S. (Autor:in) / Goddard, P. (Autor:in) / Rossiter, J. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    227200 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Learning GP-BayesFilters via Gaussian process latent variable models

    Ko, J. | British Library Online Contents | 2011


    Sample-efficient robot motion learning using Gaussian process latent variable models

    Delgado-Guerrero, Juan Antonio / Colomé, Adrià / Torras, Carme | BASE | 2020

    Freier Zugriff

    Classification of Streetsigns Using Gaussian Process Latent Variable Models

    Wober, Wilfried / Aburaia, Mohamed / Olaverri-Monreal, Cristina | IEEE | 2019


    Unsupervised texture segmentation based on latent topic assignment

    Feng, H. / Jiang, Z. / Shi, J. | British Library Online Contents | 2013


    Contextual policy search for micro-data robot motion learning through covariate Gaussian process latent variable models

    Delgado-Guerrero, Juan Antonio / Colomé, Adrià / Torras, Carme | BASE | 2020

    Freier Zugriff