Data Augmentation for Improving Proliferative Diabetic Retinopathy Detection in Eye Fundus Images

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Teresa Araújo (Autor) Outra; Guilherme Aresta (Autor) Outra; Luís Mendonça (Autor) Outra; Susana Penas (Autor) FMUP; Carolina Maia (Autor) Outra; Ângela Carneiro (Autor) FMUP; Ana Maria Mendonça (Autor) FEUP; Aurélio Campilho (Autor) FEUP

Revista

Título: IEEE Access

Vol. 8

Páginas: 182462-182474

ISSN: 2169-3536

Editora: IEEE

Indexação

ISI Web of Knowledge - 24 Citações

Scopus - 50 Citações

Outras Informações

ID Authenticus: P-00S-TZR

DOI: 10.1109/access.2020.3028960

Abstract (EN): Proliferative diabetic retinopathy (PDR) is an advanced diabetic retinopathy stage, characterized by neovascularization, which leads to ocular complications and severe vision loss. However, the available DR-labeled retinal image datasets have a small representation of images of the severest DR grades, and thus there is lack of PDR cases for training DR grading models. Additionally, the criteria for labelling these images in the publicly available datasets is not always clear, with some images which do not show typical PDR lesions being labeled as PDR due to the presence of photo-coagulation treatment and laser marks. This problem, together with the datasets' high class imbalance, leads to a limited variability of the samples, which the typical data augmentation and class balancing cannot fully mitigate. We propose a heuristic-based data augmentation scheme based on the synthesis of neovessel (NV)-like structures that compensates for the lack of PDR cases in DR-labeled datasets. The proposed neovessel generation algorithm relies on the general knowledge of common location and shape of these structures. NVs are generated and introduced in pre-existent retinal images which can then be used for enlarging deep neural networks' training sets. The data augmentation scheme was tested on multiple datasets, and allows to improve the model's capacity to detect NVs.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13