Learning from Only Positive and Unlabeled Data to Detect Lesions in Vascular CT Images

  • Maria A. Zuluaga
  • Don Hush
  • Edgar J. F. Delgado Leyton
  • Marcela Hernández Hoyos
  • Maciej Orkisz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Detecting vascular lesions is an important task in the diagnosis and follow-up of the coronary heart disease. While most existing solutions tackle calcified and non-calcified plaques separately, we present a new algorithm capable of detecting both types of lesions in CT images. It builds up on a semi-supervised classification framework, in which the training set is made of both unlabeled data and a small amount of data labeled as normal. Our method takes advantage of the arrival of newly acquired data to re-train the classifier and improve its performance. We present results on synthetic data and on datasets from 15 patients. With a small amount of labeled training data our method achieved a 89.8% true positive rate, which is comparable to state-of-the-art supervised methods, and the performance can improve after additional iterations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maria A. Zuluaga
    • 1
    • 2
  • Don Hush
    • 3
  • Edgar J. F. Delgado Leyton
    • 1
    • 2
  • Marcela Hernández Hoyos
    • 2
  • Maciej Orkisz
    • 1
  1. 1.INSERM U1044CREATIS; Université de Lyon; Université Lyon 1; INSA-Lyon; CNRS UMR5220VilleurbanneFrance
  2. 2.Grupo Imagine, Grupo de Ingeniería BiomédicaUniversidad de los AndesBogotáColombia
  3. 3.Los Alamos National LaboratoryLos AlamosUSA

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