Search Results for author: Qinji Yu

Found 6 papers, 2 papers with code

Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning

1 code implementation19 Jul 2023 Qinji Yu, Nan Xi, Junsong Yuan, Ziyu Zhou, Kang Dang, Xiaowei Ding

To tackle the source data-absent problem, we present a novel two-stage source-free domain adaptation (SFDA) framework for medical image segmentation, where only a well-trained source segmentation model and unlabeled target data are available during domain adaptation.

Contrastive Learning Image Segmentation +5

Region and Spatial Aware Anomaly Detection for Fundus Images

no code implementations7 Mar 2023 Jingqi Niu, Shiwen Dong, Qinji Yu, Kang Dang, Xiaowei Ding

ReSAD transfers a pre-trained model to extract the features of normal fundus images and applies the Region-and-Spatial-Aware feature Combination module (ReSC) for pixel-level features to build a memory bank.

Anomaly Detection

Coarse Retinal Lesion Annotations Refinement via Prototypical Learning

no code implementations30 Aug 2022 Qinji Yu, Kang Dang, Ziyu Zhou, Yongwei Chen, Xiaowei Ding

Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data.

Lesion Segmentation

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

no code implementations14 Feb 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu

However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.

A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation

1 code implementation18 Mar 2021 Qinji Yu, Kang Dang, Nima Tajbakhsh, Demetri Terzopoulos, Xiaowei Ding

Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data.

Image Segmentation Medical Image Segmentation +3

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