Search Results for author: Yijin Huang

Found 8 papers, 6 papers with code

Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement

1 code implementation8 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.

Image Enhancement

SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading

1 code implementation20 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.

Contrastive Learning Diabetic Retinopathy Grading +1

AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

1 code implementation27 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).

Data Augmentation Domain Generalization +4

LesionPaste: One-Shot Anomaly Detection for Medical Images

no code implementations12 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

Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation

2 code implementations27 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.

Data Augmentation Diabetic Retinopathy Grading

Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images

2 code implementations17 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.

Contrastive Learning Data Augmentation +1

BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images

1 code implementation10 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.


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