Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems. Existing RIQA methods focus on the RGB color-space and are developed based on small datasets with binary quality labels (i.e., `Accept' and `Reject'). In this paper, we first re-annotate an Eye-Quality (EyeQ) dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., `Good', `Usable' and `Reject') for evaluating RIQA methods. Our RIQA dataset is characterized by its large-scale size, multi-level grading, and multi-modality. Then, we analyze the influences on RIQA of different color-spaces, and propose a simple yet efficient deep network, named Multiple Color-space Fusion Network (MCF-Net), which integrates the different color-space representations at both a feature-level and prediction-level to predict image quality grades. Experiments on our EyeQ dataset show that our MCF-Net obtains a state-of-the-art performance, outperforming the other deep learning methods. Furthermore, we also evaluate diabetic retinopathy (DR) detection methods on images of different quality, and demonstrate that the performances of automated diagnostic systems are highly dependent on image quality.Comment: Accepted by MICCAI 2019. Corrected two typos in Table 1 as: (1) in training set, the number of "Usable + All" should be '1,876'; (2) In testing set, the number of "Total + DR-0" should be '11,362'. Project page: https://github.com/hzfu/Eye

Theoretical study of the insulating oxides and nitrides: SiO2, GeO2, Al2O3, Si3N4, and Ge3N4

An extensive theoretical study is performed for wide bandgap crystalline oxides and nitrides, namely, SiO_{2}, GeO_{2}, Al_{2}O_{3}, Si_{3}N_{4}, and Ge_{3}N_{4}. Their important polymorphs are considered which are for SiO_{2}: $\alpha$-quartz, $\alpha$- and $\beta$-cristobalite and stishovite, for GeO_{2}: $\alpha$-quartz, and rutile, for Al_{2}O_{3}: $\alpha$-phase, for Si_{3}N_{4} and Ge_{3}N_{4}: $\alpha$- and $\beta$-phases. This work constitutes a comprehensive account of both electronic structure and the elastic properties of these important insulating oxides and nitrides obtained with high accuracy based on density functional theory within the local density approximation. Two different norm-conserving \textit{ab initio} pseudopotentials have been tested which agree in all respects with the only exception arising for the elastic properties of rutile GeO_{2}. The agreement with experimental values, when available, are seen to be highly satisfactory. The uniformity and the well convergence of this approach enables an unbiased assessment of important physical parameters within each material and among different insulating oxide and nitrides. The computed static electric susceptibilities are observed to display a strong correlation with their mass densities. There is a marked discrepancy between the considered oxides and nitrides with the latter having sudden increase of density of states away from the respective band edges. This is expected to give rise to excessive carrier scattering which can practically preclude bulk impact ionization process in Si_{3}N_{4} and Ge_{3}N_{4}.Comment: Published version, 10 pages, 8 figure