Titlebook: Computer Analysis of Images and Patterns; 20th International C Nicolas Tsapatsoulis,Andreas Lanitis,Andreas Panay Conference proceedings 20

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,Report on normal bundles of curves in ?3,dataset with around 2000 new annotations, and analyze 1) the effect of different data augmentation techniques on learning the skin lesion symmetry classification task, and 2) how the learning of this task is affected when combined with the classification of its malignancy in a multitask learning env

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https://doi.org/10.1007/BFb0099500ess of acquiring such real-world data is laborious, prompting the exploration of new research directions such as synthetic data generation. In this study, we assess the capability of two distinct synthetic data generating techniques utilising stable diffusion, namely, (1) Prompt engineering of an es

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Dimension formulas related to a tame quiver,rpretable and efficient texture classification framework that considers colour or channel information and does not require much data to produce accurate results. Therefore, such a classifier can be suitable for medical applications and resource-limited hardware. We base our work on a Generalized Mat

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