@inproceedings{52bf2aa165804cc29c3615ea31bad7f2,
title = "EyeQual: Accurate, explainable, retinal image quality assessment",
abstract = "Given a retinal image, can we automatically determine whether it is of high quality (suitable for medical diagnosis)? Can we also explain our decision, pinpointing the region or regions that led to our decision? Images from human retinas are vital for the diagnosis of multiple health issues, like hypertension, diabetes, and Alzheimer's; low quality images may force the patient to come back again for a second scanning, wasting time and possibly delaying treatment. However, existing retinal image quality assessment methods are either black boxes without explanations of the results or depend heavily on feature engineering or on complex and error-prone anatomical structures' segmentation. Therefore, we propose EyeQual, that solves exactly this problem. EyeQual is novel, fast for inference, accurate and explainable, pinpointing low-quality regions on the image. We evaluated EyeQual on two real datasets where it achieved 100\% accuracy taking just 36 milliseconds for each image.",
keywords = "Medical Image Analysis, Multiple Instance Learning, Quality Assessment, Retinal Image, Weakly Supervised Learning",
author = "Pedro Costa and Aurelio Campilho and Bryan Hooi and Asim Smailagic and Kris Kitani and Shenghua Liu and Christos Faloutsos and Adrian Galdran",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 ; Conference date: 18-12-2017 Through 21-12-2017",
year = "2017",
doi = "10.1109/ICMLA.2017.0-140",
language = "English",
series = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "323--330",
editor = "Xuewen Chen and Bo Luo and Feng Luo and Vasile Palade and Wani, \{M. Arif\}",
booktitle = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
address = "United States",
}
