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Proceedings Paper

Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images
Author(s): Clara I. Sánchez; Meindert Niemeijer; Thessa Kockelkorn; Michael D. Abràmoff; Bram van Ginneken
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Paper Abstract

Computer-aided Diagnosis (CAD) systems for the automatic identification of abnormalities in retinal images are gaining importance in diabetic retinopathy screening programs. A huge amount of retinal images are collected during these programs and they provide a starting point for the design of machine learning algorithms. However, manual annotations of retinal images are scarce and expensive to obtain. This paper proposes a dynamic CAD system based on active learning for the automatic identification of hard exudates, cotton wool spots and drusen in retinal images. An uncertainty sampling method is applied to select samples that need to be labeled by an expert from an unlabeled set of 4000 retinal images. It reduces the number of training samples needed to obtain an optimum accuracy by dynamically selecting the most informative samples. Results show that the proposed method increases the classification accuracy compared to alternative techniques, achieving an area under the ROC curve of 0.87, 0.82 and 0.78 for the detection of hard exudates, cotton wool spots and drusen, respectively.

Paper Details

Date Published: 3 March 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601I (3 March 2009); doi: 10.1117/12.813679
Show Author Affiliations
Clara I. Sánchez, Univ. Medisch Ctr. Utrecht (Netherlands)
Meindert Niemeijer, The Univ. of Iowa (United States)
Thessa Kockelkorn, Univ. Medisch Ctr. Utrecht (Netherlands)
Michael D. Abràmoff, The Univ. of Iowa (United States)
Veterans Affairs Medical Ctr. (United States)
Bram van Ginneken, Univ. Medisch Ctr. Utrecht (Netherlands)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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