Explainable Image Quality Assessments in Teledermatological Photography
- URL: http://arxiv.org/abs/2209.04699v1
- Date: Sat, 10 Sep 2022 15:48:28 GMT
- Title: Explainable Image Quality Assessments in Teledermatological Photography
- Authors: Raluca Jalaboi, Ole Winther, Alfiia Galimzianova
- Abstract summary: Image quality is a crucial factor in the success of teledermatological consultations.
Up to 50% of images sent by patients have quality issues.
We introduce ImageQX, a first of its kind explainable image quality assessor.
- Score: 8.772468575761366
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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