title: Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography.
abstract: OBJECTIVE
  We investigate the influence of the clinical context of high-resolution computed tomography (HRCT) images of the chest on tissue classification.
  
  
  METHODS
  2D regions of interest in HRCT axial slices from patients affected with an interstitial lung disease are automatically classified into five classes of lung tissue. Relevance of the clinical parameters is studied before fusing them with visual attributes. Two multimedia fusion techniques are compared: early versus late fusion. Early fusion concatenates features in one single vector, yielding a true multimedia feature space. Late fusion consisting of the combination of the probability outputs of two support vector machines.
  
  
  CONCLUSIONS
  The late fusion scheme allowed a maximum of 84% correct predictions of testing instances among the five classes of lung tissue. This represents a significant improvement of 10% compared to a pure visual-based classification. Moreover, the late fusion scheme showed high robustness to the number of clinical parameters used, which suggests that it is appropriate for mining clinical attributes with missing values in clinical routine.
authors.affiliation: Medical Informatics Service, Geneva University Hospitals and University of Geneva, Geneva, Switzerland. Adrien.Depeursinge@sim.hcuge.ch
authors.forename: Adrien
authors.initials: A
authors.lastname: Depeursinge
authors.forename: Daniel
authors.initials: D
authors.lastname: Racoceanu
authors.forename: Jimison
authors.initials: J
authors.lastname: Iavindrasana
authors.forename: Gilles
authors.initials: G
authors.lastname: Cohen
authors.forename: Alexandra
authors.initials: A
authors.lastname: Platon
authors.forename: Pierre-Alexandre
authors.initials: PA
authors.lastname: Poletti
authors.forename: Henning
authors.initials: H
authors.lastname: Müller
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D008297:Male; D008490:Medical Informatics; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011379:Prognosis; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed