Diabetic Retinopathy Research Published on PlosOne

RECOD research on automated screening of Diabetic Retinopathy — “Advancing Bag-of-Visual-Words Representations for Lesion Classification in Retinal Images” — has just been published on the leading open access journal PlosOne.

The research work was performed in cooperation by  Ph.D. student Ramon Pires,  Profs. Anderson Rocha, Eduardo Valle and Jacques Weiner (all from RECOD Lab.), and Prof. Herbert Jelinek (Charles Sturt University, Australia).

Here’s the abstract :

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.22.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

The full text of the article is available online.

This article inaugurates RECOD’s policy on improving the reproducibility of our published results, by making publicly available both data and code. As much as allowable by copyright law and by ethical practice (e.g., respect to the privacy of patients and members of the public) RECOD is engaged in increasing the number of papers with public datasets and or code, and helping other researchers to reproduce our results with as little hassle as possible.

 The retinopathy datasets are hosted in figshare, under DOI 10.6084/m9.figshare.953671.

The code used in the article is hosted in github. in the repository piresramon/retina.BoVW.plosone.git.

About eduardovalle

Professor at FEEC/UNICAMP, Brazil. Researcher on Machine Learning/Computer Vision, with emphasis on Health & Education applications.
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2 Responses to Diabetic Retinopathy Research Published on PlosOne

  1. Pingback: Your PLOS ONE Article is in the Top 25% Most Cited | RECOD — Reasoning for Complex Data

  2. Pingback: Your PLOS ONE Article is in the Top 25% Most Cited | Ramon Pires

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