Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images

Clara I. Sánchez; Meindert Niemeijer; Thessa Kockelkorn; Michael D. Abràmoff M.D.; Bram van Ginneken

doi:10.1117/12.813679

3 March 2009 Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images

Clara I. Sánchez, Meindert Niemeijer, Thessa Kockelkorn, Michael D. Abràmoff M.D., Bram van Ginneken

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72601I (2009) https://doi.org/10.1117/12.813679
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Clara I. Sánchez, Meindert Niemeijer, Thessa Kockelkorn, Michael D. Abràmoff M.D., and Bram van Ginneken "Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601I (3 March 2009); https://doi.org/10.1117/12.813679

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