1 code implementation • 8 Mar 2023 • Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang
In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images.
no code implementations • 20 Dec 2022 • Juntao Chen, Li Lin, Pujin Cheng, Yijin Huang, Xiaoying Tang
Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications.
1 code implementation • 20 Oct 2022 • Yijin Huang, Junyan Lyu, Pujin Cheng, Roger Tam, Xiaoying Tang
Specifically, two saliency-guided learning tasks are employed in SSiT: (1) We conduct saliency-guided contrastive learning based on the momentum contrast, wherein we utilize fundus images' saliency maps to remove trivial patches from the input sequences of the momentum-updated key encoder.
1 code implementation • 27 Jul 2022 • Junyan Lyu, Yiqi Zhang, Yijin Huang, Li Lin, Pujin Cheng, Xiaoying Tang
To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG).
no code implementations • 12 Mar 2022 • Weikai Huang, Yijin Huang, Xiaoying Tang
Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training.
Semi-supervised Anomaly Detection supervised anomaly detection
2 code implementations • 27 Oct 2021 • Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Roger Tam, Xiaoying Tang
To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components.
2 code implementations • 17 Jul 2021 • Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Xiaoying Tang
Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading.
1 code implementation • 10 Jul 2021 • Li Lin, Zhonghua Wang, Jiewei Wu, Yijin Huang, Junyan Lyu, Pujin Cheng, Jiong Wu, Xiaoying Tang
Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier.