Month: August 2019

MICCAI2019: Fully Deep Learning for Slit-lamp Photo based Nuclear Cataract Grading

Our MICCAI2019 paper on automated nuclear cataract grading is online.

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 second, 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.

miccai2019-nuclear-cataract-grading

Chaoxi Xu, Xiangjia Zhu, Wenwen He, Yi Lu, Xixi He, Zongjiang Shang, Jun Wu, Keke Zhang, Yinglei Zhang, Xianfang Rong, Zhennan Zhao, Lei Cai, Dayong Ding, Xirong Li: Fully Deep Learning for Slit-lamp Photo based Nuclear Cataract Grading. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019, (early accept).

MICCAI 2019: Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization

Our MICCAI’19 paper on multi-modal age-related macular degeneration (AMD) categorization is online.

This paper studies automated categorization of age-related macular degeneration (AMD) given a multi-modal input, which consists of a color fundus image and an optical coherence tomography (OCT) image from a specific eye. Previous work uses a traditional method, comprised of feature extraction and classifier training that cannot be optimized jointly. By contrast, we propose a two-stream convolutional neural network (CNN) that is end-to-end. The CNN’s fusion layer is tailored to the need of fusing information from the fundus and OCT streams. For generating more multi-modal training instances, we introduce Loose Pair training, where a fundus image and an OCT image are paired based on class labels rather than eyes. Moreover, for a visual interpretation of how the individual modalities make contributions, we extend the class activation mapping technique to the multi-modal scenario. Experiments on a real-world dataset collected from an outpatient clinic justify the viability of our proposal for multi-modal AMD categorization.

Weisen Wang, Zhiyan Xu, Weihong Yu, Jianchun Zhao, Jingyuan Yang, Feng He, Zhikun Yang, Di Chen, Dayong Ding, Youxin Chen, Xirong Li: Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019, (early accept).