# Explainable Image Quality Assessments in Teledermatological Photography

arXiv: https://arxiv.org/abs/2209.04699 · HTML: https://mlsanity.com/paper/2209.04699/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Image Quality Assessment using Contrastive Learning](https://mlsanity.com/paper/2110.13266/) (2021)
- [On the Use of Deep Learning for Blind Image Quality Assessment](https://mlsanity.com/paper/1602.05531/) (2016)
- [DermX: an end-to-end framework for explainable automated dermatological diagnosis](https://mlsanity.com/paper/2202.06956/) (2022)
- [TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos](https://mlsanity.com/paper/2010.02086/) (2020)

## Built on by (most cited first, 4 of 4)

- [Half of an image is enough for quality assessment](https://mlsanity.com/paper/2301.12891/) (2023)
- [Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks](https://mlsanity.com/paper/2302.12084/) (2023)
- [Enhanced Dermatology Image Quality Assessment via Cross-Domain Training](https://mlsanity.com/paper/2506.16116/) (2025)
- [Beyond Blur: A Semantic Tri-view Pipeline for Teledermatology Gradability via Skin Micro-relief](https://mlsanity.com/paper/2609.03095/) (2026)

## Abstract (arXiv)

Image quality is a crucial factor in the effectiveness and efficiency 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 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 26,635 photographs and validated on 9,874 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 and 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 +- 0.01, which places it within standard deviation of the pairwise inter-rater F1-score of 0.77 +- 0.07. For poor image quality explanations, our method obtains F1-scores of between 0.37 +- 0.01 and 0.70 +- 0.01, similar to the inter-rater pairwise F1-score of between 0.24 +- 0.15 and 0.83 +- 0.06. 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 enable a better, faster flow for remote consultations.