title: Learning from only positive and unlabeled data to detect lesions in vascular CT images.
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.
authors: CREATIS, Université de Lyon, Université Lyon 1, INSA-Lyon, CNRS UMR5220, INSERM U1044, F-69621 Villeurbanne, France.
authors: Maria A
authors: MA
authors: Zuluaga
authors: Don
authors: D
authors: Hush
authors: Edgar J F
authors: EJ
authors: Delgado Leyton
authors: Marcela
authors: M
authors: Hernández Hoyos
authors: Maciej
authors: M
authors: Orkisz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001808:Blood Vessels / Q000473:pathology*; D003331:Coronary Vessels / Q000473:pathology; D006801:Humans; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014652:Vascular Diseases / Q000473:pathology*