6 papers · 1 filter
PARAGraph: Pathology-Anatomy-Aware Hierarchical Graph for Diabetic Retinopathy Grading
Ziyang Zhang, Yuankai Huo, Yalin Zheng +1
Diabetic retinopathy (DR) remains a leading cause of vision loss among working-age adults worldwide, making reliable severity grading clinically important. Despite strong performan…
Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification
Feixiang Zhou, Jianyang Xie, Zhuangzhi Gao +10
The missing-modality problem poses a significant challenge in image-tabular multimodal learning across a wide range of multimedia applications, including product understanding, rec…
GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation
Feixiang Zhou, Zhuangzhi Gao, He Zhao +5
Accurate segmentation of tubular structures, such as vascular networks, plays a critical role in various medical domains. A remaining significant challenge in this task is structur…
Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
Jianyang Xie, Yitian Zhao, Yanda Meng +3
Spatial-temporal graph convolutional networks (ST-GCNs) showcase impressive performance in skeleton-based human action recognition (HAR). However, despite the development of numero…
Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis
Chengzhi Liu, Zile Huang, Zhe Chen +6
Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shortages, low-quality data and data…
CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-aware Prompting
Qinkai Yu, Jianyang Xie, Anh Nguyen +6
Diabetic retinopathy (DR) is a complication of diabetes and usually takes decades to reach sight-threatening levels. Accurate and robust detection of DR severity is critical for th…