Abstract
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, blur, low resolution, and distance issues. ImageQX was trained on 26635 photographs and validated on 9874 photographs, each annotated with image quality labels and poor image quality explanations by up to 12 board-certified dermatologists. The photographic images were taken between 2017-2019 using a mobile skin disease tracking application accessible worldwide. Our method achieves expert-level performance for both image quality assessment and poor image quality explanation. For image quality assessment, ImageQX obtains a macro F1-score of 0.73 which places it within standard deviation of the pairwise inter-rater F1-score of 0.77. For poor image quality explanations, our method obtains F1-scores of between 0.37 and 0.70, similar to the inter-rater pairwise F1-score of between 0.24 and 0.83. Moreover, with a size of only 15 MB, ImageQX is easily deployable on mobile devices. With an image quality detection performance similar to that of dermatologists, incorporating ImageQX into the teledermatology flow can reduce the image evaluation burden on dermatologists, while at the same time reducing the time to diagnosis and treatment for patients. We introduce ImageQX, a first of its kind explainable image quality assessor which leverages domain expertise to improve the quality and efficiency of dermatological care in a virtual setting.
Abstract (translated)
URL
https://arxiv.org/abs/2209.04699
