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Wyniki wyszukiwania dla: IMAGE ANNOTATION
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Image simulation and annotation for color blinded
PublikacjaIn this paper methods for image simulation as seen by a color blinded and a method for constructing images of perceived color difference are presented. The work is also focused on the interactive color description of an image contents. As a result, the individuals having problems with color discrimination can identify colors in an image.W artykule prezentowane są metody symulacji kolorów w obrazach postrzeganych przez osoby ze...
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Image simulation and annotation for color blinded
PublikacjaW pracy przedstawiono metody symulacji obrazów widzianych przez osoby ze ślepota barw. Ukazano również metody tworzenia obrazów ukazujących różnicę w percepcji kolorów pomiędzy normalnym obserwatorem a osobą ze ślepotą barw. W artykule opisano również metodę interaktywnego opisu koloru wskazywanego piksela obrazu. W rezultacie użytkownik ze ślepota barw może uzyskać informacje opisowe o występujących w obrazie kolorach.
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Medical Image Dataset Annotation Service (MIDAS)
PublikacjaMIDAS (Medical Image Dataset Annotation Service) is a custom-tailored tool for creating and managing datasets either for deep learning, as well as machine learning or any form of statistical research. The aim of the project is to provide one-fit-all platform for creating medical image datasets that could easily blend in hospital's workflow. In our work, we focus on the importance of medical data anonimization, discussing the...
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Segmentation Quality Refinement in Large-Scale Medical Image Dataset with Crowd-Sourced Annotations
PublikacjaDeployment of different techniques of deep learning including Convolutional Neural Networks (CNN) in image classification systems has accomplished outstanding results. However, the advantages and potential impact of such a system can be completely negated if it does not reach a target accuracy. To achieve high classification accuracy with low variance in medical image classification system, there is needed the large size of the...
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Semantic segmentation training using imperfect annotations and loss masking
PublikacjaOne of the most significant factors affecting supervised neural network training is the precision of the annotations. Also, in a case of expert group, the problem of inconsistent data annotations is an integral part of real-world supervised learning processes, well-known to researchers. One practical example is a weak ground truth delineation for medical image segmentation. In this paper, we have developed a new method of accurate...