Diabetic Retinopathy Paper Accepted at IEEE EMBC’14

(This entry was crossposted with minor modifications from my lab’s blog.)

Our cooperative work on Diabetic Retinopathy has produced a new paper, now in the IEEE Engineering in Medicine and Biology Conference ! This new work explores the BossaNova representation — an state-of-the-art extension to the bags-of-words model in the task of Diabetic Retinopathy classification.

Take at look at the abstract :

The biomedical community has shown a continued interest in automated detection of Diabetic Retinopathy (DR), with new imaging techniques, evolving diagnostic criteria, and advancing computing methods. Existing state of the art for detecting DR-related lesions tends to emphasize different, specific approaches for each type of lesion. However, recent research has aimed at general frameworks adaptable for large classes of lesions. In this paper, we follow this latter trend by exploring a very flexible framework, based upon two-tiered feature extraction (low-level and mid-level) from images and Support Vector Machines. The main contribution of this work is the evaluation of BossaNova, a recent and powerful mid-level image characterization technique, which we contrast with previous art based upon classical Bag of Visual Words (BoVW). The new technique using BossaNova achieves a detection performance (measured by area under the curve — AUC) of 96.4% for hard exudates, and 93.5% for red lesions using a cross-dataset training/testing protocol.

ROC curves for hard exudates using class-based codebooks and comparing our previous approach with BoW [11] and our newly proposed technique with BossaNova. The AUCs are shown on the legend.

ROC curves for hard exudates using class-based codebooks and comparing our previous approach with BoW [11] and our newly proposed technique with BossaNova. The AUCs are shown on the legend.

The full-text preprint is available in my publications page. The conference will be held in Chicago, IL, USA from August 26 to 30, 2014.

Continuing our efforts in making our results reproducible. The datasets used in this work are publicly available at FigShare, under the DOI : 10.6084/m9.figshare.953671. The code employed will be released soon.

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