تقرير
A multi-task deep learning model for the classification of Age-related Macular Degeneration
| العنوان: | A multi-task deep learning model for the classification of Age-related Macular Degeneration |
|---|---|
| المؤلفون: | Chen, Qingyu, Peng, Yifan, Keenan, Tiarnan, Dharssi, Shazia, Agron, Elvira, Wong, Wai T., Chew, Emily Y., Lu, Zhiyong |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Age-related Macular Degeneration (AMD) is a leading cause of blindness. Although the Age-Related Eye Disease Study group previously developed a 9-step AMD severity scale for manual classification of AMD severity from color fundus images, manual grading of images is time-consuming and expensive. Built on our previous work DeepSeeNet, we developed a novel deep learning model for automated classification of images into the 9-step scale. Instead of predicting the 9-step score directly, our approach simulates the reading center grading process. It first detects four AMD characteristics (drusen area, geographic atrophy, increased pigment, and depigmentation), then combines these to derive the overall 9-step score. Importantly, we applied multi-task learning techniques, which allowed us to train classification of the four characteristics in parallel, share representation, and prevent overfitting. Evaluation on two image datasets showed that the accuracy of the model exceeded the current state-of-the-art model by > 10%. Comment: 10 pages, 5 figures, and 5 tables To appear in the Proceeding of AMIA Informatics 2019 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1812.00422 |
| رقم الأكسشن: | edsarx.1812.00422 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |