The Messidor database, which contains hundreds of eye fundus images, has been publicly distributed since 2008. It was created by the Messidor project in order to evaluate automatic lesion segmentation and diabetic retinopathy grading methods. Designing, producing and maintaining such a database entails significant costs. By publicly sharing it, one hopes to bring a valuable resource to the public research community. However, the real interest and benefit of the research community is not easy to quantify. We analyse here the feedback on the Messidor database, after more than 6 years of diffusion. This analysis should apply to other similar research databases.

The automatic detection of exudates in color eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to automatically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also detect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods.

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Graphene-Si Schottky junction solar cells are promising candidates for high-efficiency, low-cost photovoltaic applications. However, their performance enhancement is restricted by strong carrier recombination and relative low barrier height. Here, we demonstrated the successful construction of high-efficiency graphene-planar Si solar cells via modification of the Si surface with a molecule monolayer as well as tuning the interface band alignment with an organic electron blocking layer.Methylated Si showed the capability to effectively suppress the surface carrier recombination, leading to a remarkable improvement of device efficiency. The recombination was further reduced by inserting a thin P3HT organic layer; the unique band alignment could prevent electron transfer from n-Si to the graphene anode so as to minimize the current leakage. These methods, along with careful control of the graphene doping level and layer number, gave rise to a power conversion efficiency (PCE) as high as 10.56%. The scalability of the devices was further investigated by studying the device area dependent photovoltaic performance.

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