38 citations · 58 across the 11 of their papers we have counts for
11 papers
Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
Heng Li, Haojin Li, Wei Zhao +4
The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data…
Content-Preserving Diffusion Model for Unsupervised AS-OCT image Despeckling
Li Sanqian, Higashita Risa, Fu Huazhu +3
Anterior segment optical coherence tomography (AS-OCT) is a non-invasive imaging technique that is highly valuable for ophthalmic diagnosis. However, speckles in AS-OCT images can…
Label-noise-tolerant medical image classification via self-attention and self-supervised learning
Hongyang Jiang, Mengdi Gao, Yan Hu +3
Deep neural networks (DNNs) have been widely applied in medical image classification and achieve remarkable classification performance. These achievements heavily depend on large-s…
Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network
Yinglin Zhang, Ruiling Xi, Huazhu Fu +4
Robust and accurate segmentation for elongated physiological structures is challenging, especially in the ambiguous region, such as the corneal endothelium microscope image with un…
Learnable Ophthalmology SAM
Zhongxi Qiu, Yan Hu, Heng Li +1
Segmentation is vital for ophthalmology image analysis. But its various modal images hinder most of the existing segmentation algorithms applications, as they rely on training base…
Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detection
Hongyang Jiang, Jingqi Huang, Chen Tang +3
Deep neural networks (DNNs) have promoted the development of computer aided diagnosis (CAD) systems for fundus diseases, helping ophthalmologists reduce missed diagnosis and misdia…
Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning
Xiaoqing Zhang, Zunjie Xiao, Xiao Wu +4
Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency…
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module
Jiayi Zhang, Xiaoshan Chen, Zhongxi Qiu +3
Hard exudates (HE) is the most specific biomarker for retina edema. Precise HE segmentation is vital for disease diagnosis and treatment, but automatic segmentation is challenged b…
Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network
Haofeng Liu, Heng Li, Huazhu Fu +4
As an economical and efficient fundus imaging modality, retinal fundus images have been widely adopted in clinical fundus examination. Unfortunately, fundus images often suffer fro…
SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image
Yan Hu, Zhongxi Qiu, Dan Zeng +3
Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases. Ophthalmologists often require high-reso…
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
Heng Li, Haofeng Liu, Huazhu Fu +5
Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. However, for cataract patients, the fundus image always suffers quality degradation…