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20192025
most citedMulti-Modal Multi-Instance Learning for Retinal Disease Recognition

51 citations · 63 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

MIQ-SAM3D: From Single-Point Prompt to Multi-Instance Segmentation via Competitive Query Refinement

Jierui Qu, Jianchun Zhao

Accurate segmentation of medical images is fundamental to tumor diagnosis and treatment planning. SAM-based interactive segmentation has gained attention for its strong generalizat…

cs.CV2025

RDTE-UNet: A Boundary and Detail Aware UNet for Precise Medical Image Segmentation

Jierui Qu, Jianchun Zhao

Medical image segmentation is essential for computer-assisted diagnosis and treatment planning, yet substantial anatomical variability and boundary ambiguity hinder reliable deline…

cs.CV2025

A DyL-Unet framework based on dynamic learning for Temporally Consistent Echocardiographic Segmentation

Jierui Qu, Jianchun Zhao

Accurate segmentation of cardiac anatomy in echocardiography is essential for cardiovascular diagnosis and treatment. Yet echocardiography is prone to deformation and speckle noise…

cs.CV2022★ 4 cited

Lesion Localization in OCT by Semi-Supervised Object Detection

Yue Wu, Yang Zhou, Jianchun Zhao +4

Over 300 million people worldwide are affected by various retinal diseases. By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal structural changes in the…

cs.CV2021★ 51 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.CV2020★ 3 cited

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

Weisen Wang, Xirong Li, Zhiyan Xu +4

This paper tackles automated categorization of Age-related Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD…