Search Results for author: Pujin Cheng

Found 14 papers, 8 papers with code

JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection

no code implementations19 May 2023 Huaqing He, Li Lin, Zhiyuan Cai, Pujin Cheng, Xiaoying Tang

To address these issues, we propose a prior guided multi-task transformer framework for joint OD/OC segmentation and fovea detection, named JOINEDTrans.

Fovea Detection Image Segmentation +1

YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation

no code implementations11 Dec 2022 Li Lin, Linkai Peng, Huaqing He, Pujin Cheng, Jiewei Wu, Kenneth K. Y. Wong, Xiaoying Tang

With only one noisy skeleton annotation (respectively 0. 14%, 0. 03%, 1. 40%, and 0. 65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset.

Contrastive Learning Image Generation +3

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

DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network

no code implementations14 Mar 2022 Ziqi Huang, Li Lin, Pujin Cheng, Kai Pan, Xiaoying Tang

Furthermore, with only 5% paired data, the proposed DS3-Net achieves competitive performance with state-of-theart image translation methods utilizing 100% paired data, delivering an average SSIM of 0. 8947 and an average PSNR of 23. 60.

Knowledge Distillation SSIM +1

Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion

no code implementations9 Mar 2022 Ziqi Huang, Li Lin, Pujin Cheng, Linkai Peng, Xiaoying Tang

As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e. g., brain tumor segmentation) for performance enhancing.

Brain Tumor Segmentation Contrastive Learning +2

Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning

1 code implementation13 Jan 2022 Linkai Peng, Li Lin, Pujin Cheng, Ziqi Huang, Xiaoying Tang

The two models use labeled data (together with the corresponding transferred images) for supervised learning and perform collaborative consistency learning on unlabeled data.

Image Reconstruction Retinal Vessel Segmentation +2

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

LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-annotation Face Alignment

1 code implementation29 Sep 2021 Huilin Yang, Junyan Lyu, Pujin Cheng, Roger Tam, Xiaoying Tang

We innovatively propose a flexible and consistent cross-annotation face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way.

Face Alignment Metric Learning

LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Flexible and Consistent Face Alignment

no code implementations2 Aug 2021 Huilin Yang, Junyan Lyu, Pujin Cheng, Xiaoying Tang

Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate this task in a diffeomorphic registration manner and predict momenta that uniquely parameterize the deformation between initial boundary and true boundary, and then perform large deformation diffeomorphic metric mapping (LDDMM) simultaneously for curve and landmark to localize the facial landmarks.

Face Alignment Metric Learning +1

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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