Fully Deep Learning for Slit Lamp Photo Based Nuclear Cataract Grading
Age related cataract is a priority eye disease, with nuclear cataract as its most common type. This paper aims for automated nuclear cataract grading based on slit lamp photos. Different from previous efforts which rely on traditional feature extraction and grade modeling techniques, we propose in this paper a fully deep learning based solution. Given a slit lamp photo, we localize its nuclear region by Faster R CNN, followed by a ResNet 101 based grading model. In order to alleviate the issue of imbalanced data, a simple batch balancing strategy is introduced for improving the training of the grading network. Tested on a clinical dataset of 157 slit lamp photos from 39 female and 31 male patients, the proposed solution outperforms the state of the art, reducing the mean absolute error from 0.357 to 0.313. In addition, our solution processes a slit lamp photo in approximately 0.1 s, which is two order faster than the state of the art. With its effectiveness and efficiency, the new solution is promising for automated nuclear cataract grading.
MICCAI