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Explainable Image Quality Assessments in Teledermatological Photography. (arXiv:2209.04699v1 [cs.CV])
Sept. 13, 2022, 1:14 a.m. | Raluca Jalaboi, Ole Winther, Alfiia Galimzianova
cs.CV updates on arXiv.org arxiv.org
Image quality is a crucial factor in the success of teledermatological
consultations. However, up to 50% of images sent by patients have quality
issues, thus increasing the time to diagnosis and treatment. An automated,
easily deployable, explainable method for assessing image quality is necessary
to improve the current teledermatological consultation flow. We introduce
ImageQX, a convolutional neural network trained for image quality assessment
with a learning mechanism for identifying the most common poor image quality
explanations: bad framing, bad lighting, …
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