Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images

1 code implementation • 14 May 2023 • Qijie Wei, Jingyuan Yang, Bo wang, Jinrui Wang, Jianchun Zhao, Xinyu Zhao, Sheng Yang, Niranchana Manivannan, Youxin Chen, Dayong Ding, Jing Zhou, Xirong Li

This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images.

Unsupervised Domain Adaptation

Segmentation-based Information Extraction and Amalgamation in Fundus Images for Glaucoma Detection

no code implementations • 23 Sep 2022 • Yanni Wang, Gang Yang, Dayong Ding, Jianchun Zao

Glaucoma is a severe blinding disease, for which automatic detection methods are urgently needed to alleviate the scarcity of ophthalmologists.

Decision Making Segmentation

Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching

1 code implementation • 16 Jul 2022 • Jiazhen Liu, Xirong Li, Qijie Wei, Jie Xu, Dayong Ding

To attack the incompleteness of manual labeling, we propose Progressive Keypoint Expansion to enrich the keypoint labels at each training epoch.

Image Registration

Multi-Modal Multi-Instance Learning for Retinal Disease Recognition

no code implementations • 25 Sep 2021 • Xirong Li, Yang Zhou, Jie Wang, Hailan Lin, Jianchun Zhao, Dayong Ding, Weihong Yu, Youxin Chen

We propose in this paper Multi-Modal Multi-Instance Learning (MM-MIL) for selectively fusing CFP and OCT modalities.

Unsupervised Domain Expansion for Visual Categorization

2 code implementations • 1 Apr 2021 • Jie Wang, Kaibin Tian, Dayong Ding, Gang Yang, Xirong Li

In this paper we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model's performance on the source domain.

Knowledge Distillation Unsupervised Domain Adaptation +1

Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

1 code implementation • 3 Dec 2020 • Weisen Wang, Xirong Li, Zhiyan Xu, Weihong Yu, Jianchun Zhao, Dayong Ding, Youxin Chen

Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams.

Data Augmentation Image-to-Image Translation

Learn to Segment Retinal Lesions and Beyond

1 code implementation • 25 Dec 2019 • Qijie Wei, Xirong Li, Weihong Yu, Xiao Zhang, Yongpeng Zhang, Bojie Hu, Bin Mo, Di Gong, Ning Chen, Dayong Ding, Youxin Chen

This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading.

General Classification Image Classification +4

Hierarchical Attention Networks for Medical Image Segmentation

no code implementations • 20 Nov 2019 • Fei Ding, Gang Yang, Jinlu Liu, Jun Wu, Dayong Ding, Jie Xv, Gangwei Cheng, Xirong Li

Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.

Image Segmentation Medical Image Segmentation +2

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

1 code implementation • 28 Jul 2019 • Weisen Wang, Zhiyan Xu, Weihong Yu, Jianchun Zhao, Jingyuan Yang, Feng He, Zhikun Yang, Di Chen, Dayong Ding, Youxin Chen, Xirong Li

The CNN's fusion layer is tailored to the need of fusing information from the fundus and OCT streams.