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This dataset, titled "Synthetic Sand Boil Dataset for Levee Monitoring: Generated Using DreamBooth Diffusion Models," provides a comprehensive collection of synthetic images designed to facilitate the study and development of semantic segmentation models for sand boil detection in levee systems. Sand boils, a critical factor in levee integrity, pose significant risks during floods, necessitating accurate and efficient monitoring solutions.
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This dataset includes the raw data and analyzed data for an IEEE TvCg article:
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PAPILA dataset contains fundus images and clinicaldata from 244 patients, with images from both eyes of each patient. This dataset is specifically designed to support research on early glaucoma diagnosis by leveraging comprehensive data from both eyes. Additionally, it includes segmentation information for each patient’s optic disc and cup, alongside diagnostic outcomes based on clinical data. For our analysis, we focused on images labeled as normal (0) and glaucoma (1),selecting data from 210 patients.
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Chronic wounds pose an ongoing health concern globally, largely due to the prevalence of conditions such as diabetes and leprosy's disease. The standard method of monitoring these wounds involves visual inspection by healthcare professionals, a practice that could present challenges for patients in remote areas with inadequate transportation and healthcare infrastructure. This has led to the development of algorithms designed for the analysis and follow-up of wound images, which perform image-processing tasks such as classification, detection, and segmentation.
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