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In the realm of solar photovoltaic system image segmentation, existing deep learning networks focus almost exclusively on single image sources both in terms of sensors used and image resolution. This often prevents the wide deployment of such networks. Our research introduces a novel approach to train a network on a diverse range of image data, spanning UAV, aerial, and satellite imagery at both native and aggregated resolutions of 0.1 m, 0.2 m, 0.3 m, 0.8 m, 1.6 m, and 3.2 m. Using extensive hyperparameter tuning, we first determined the best possible parameter combinations for the network based on the DeepLabV3 ResNet101 architecture. We then trained a model using the wide range of different image sources. The final network offers several advantages. It outperforms networks trained with single image sources in multiple test applications as measured by the F1-Score (95.27%) and IoU (91.04%). The network is also able to work with a variety of target imagery due to the fact that a diverse range of image data was used to train it. The model is made freely available for further applications.

Introducción: el complejo esclerosis tuberosa (CET) es un síndrome neurocutáneo de herencia autosómica dominante (AD), caracterizado por la formación de múltiples hamartomas debido a la mutación de los genes TSC1 y TSC2 encargados de codificar proteínas supresoras de tumores. El CET es una enfermedad multisistémica, sin embargo, el diagnóstico se fundamenta en el compromiso neurológico, lesiones en piel y los hallazgos imagenológicos. Caso clínico: adolescente procedente de Cartagena quien a los cuatro meses se le diagnosticó Síndrome de West, manejado con Vigabatrin, posteriormente presentó epilepsia focal motora. En el examen físico se encontraron máculas hipocrómicas múltiples y angiofibromas faciales. La tomografía axial de cráneo (TAC) mostró tubérculo en región frontotemporal izquierda y nódulos subependimarios. Basado en estos criterios se hace diagnóstico de CET. Conclusión: el diagnóstico de CET es un reto para el médico, depende de una excelente historia clínica y un examen físico completo que incluye la búsqueda de lesiones características en la piel, estas pueden detectarse desde el nacimiento, al igual que los hallazgos imagenológicos detectables incluso in útero. Las pruebas genéticas, aunque son confirmatorias, no descartan la enfermedad. Rev.cienc.biomed. 2015;6(2):369-375

Deep learning (DL)—in particular convolutional neural networks (CNN)—methods are widely spread in object detection and recognition of remote sensing images. In the domain of DL, there is a need for large numbers of training samples. These samples are mostly generated based on manual identification. Identifying and labelling these objects is very time-consuming. The developed approach proposes a partially automated procedure for the sample creation and avoids manual labelling of rooftop photovoltaic (PV) systems. By combining address data of existing rooftop PV systems from the German Plant Register, the Georeferenced Address Data and the Official House Surroundings Germany, a partially automated generation of training samples is achieved. Using a selection of 100,000 automatically generated samples, a network using a RetinaNet-based architecture combining ResNet101, a feature pyramid network, a classification and a regression network is trained, applied on a large area and post-filtered by intersection with additional automatically identified locations of existing rooftop PV systems. Based on a proof-of-concept application, a second network is trained with the filtered selection of approximately 51,000 training samples. In two independent test applications using high-resolution aerial images of Saarland in Germany, buildings with PV systems are detected with a precision of at least 92.77 and a recall of 84.47.

Introduction: tuberous sclerosis complex (TSC) is a neurocutaneous syndrome autosomal dominant (AD), characterized by multiple hamartomas formation due to mutation of the TSC1 and TSC2 genes responsible for encoding tumor suppressor proteins. TSC is a multisystem disease; however, the diagnosis is based on the neurological involvement, skin lesions and imaging findings. Clinical case: a male teenager from Cartagena who at four months of age was diagnosed with West syndrome, managed with Vigabatrin, subsequently, he presented focal motor epilepsy. Macules and facial angiofibromas were found in physical examination. Cranial tomography scan (TAC) showed left frontotemporal region tuber and subependymal nodules. Based on these criteria TSC diagnosis was carried out. Conclusion: the TSC is a diagnostic challenge for the physician, it depends on excellent medical history and a complete physical examination even the search of characteristic lesions in the skin, they can be detected from the time of birth, just as detectable imaging findings even in uterus. Genetic tests do not rule the disease, although, they are confirmatory. Rev.cienc.biomed. 2015;6(2):369-375 KEYWORDS Epilepsy; Tuberous sclerosis; Tuberous; Skin lesions.