activity
20192022
most citedMulti-Modal Multi-Instance Learning for Retinal Disease Recognition

51 citations · 62 across the 4 of their papers we have counts for

collaborators

6 papers

eess.IV2022

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

Yanni Wang, Gang Yang, Dayong Ding +1

Glaucoma is a severe blinding disease, for which automatic detection methods are urgently needed to alleviate the scarcity of ophthalmologists. Many works have proposed to employ d…

cs.CV202151 cited

Multi-Modal Multi-Instance Learning for Retinal Disease Recognition

Xirong Li, Yang Zhou, Jie Wang +5

This paper attacks an emerging challenge of multi-modal retinal disease recognition. Given a multi-modal case consisting of a color fundus photo (CFP) and an array of OCT B-scan im…

cs.CV20211 cited

Unsupervised Domain Expansion for Visual Categorization

Jie Wang, Kaibin Tian, Dayong Ding +2

Expanding visual categorization into a novel domain without the need of extra annotation has been a long-term interest for multimedia intelligence. Previously, this challenge has b…

cs.CV2019

Learn to Segment Retinal Lesions and Beyond

Qijie Wei, Xirong Li, Weihong Yu +8

Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a m…

cs.CV201910 cited

Hierarchical Attention Networks for Medical Image Segmentation

Fei Ding, Gang Yang, Jinlu Liu +5

The medical image is characterized by the inter-class indistinction, high variability, and noise, where the recognition of pixels is challenging. Unlike previous self-attention bas…

eess.IV2019

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

Weisen Wang, Zhiyan Xu, Weihong Yu +8

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 tom…