Search Results for author: Xiaowei Ding

Found 14 papers, 3 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

MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan

no code implementations12 Mar 2022 Weinan Song, Gaurav Fotedar, Nima Tajbakhsh, Ziheng Zhou, Lei He, Xiaowei Ding

Furthermore, we take the transfer results as additional training data for fluid segmentation to prove the advantage of our model indirectly, i. e., in the task of data adaptation and augmentation.

Anatomy Data Augmentation +2

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

Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts

no code implementations15 Apr 2020 Gaurav Fotedar, Nima Tajbakhsh, Shilpa Ananth, Xiaowei Ding

In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student semi-supervised paradigm.

Retinal Vessel Segmentation

ErrorNet: Learning error representations from limited data to improve vascular segmentation

no code implementations10 Oct 2019 Nima Tajbakhsh, Brian Lai, Shilpa Ananth, Xiaowei Ding

In this paper, we propose a segmentation framework called ErrorNet, which learns to correct these segmentation mistakes through the repeated process of injecting systematic segmentation errors to the segmentation result based on a learned shape prior, followed by attempting to predict the injected error.

Domain Adaptation Retinal Vessel Segmentation +1

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation

no code implementations27 Aug 2019 Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding

The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.

Image Segmentation Medical Image Segmentation +2

Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data

no code implementations25 Jan 2019 Nima Tajbakhsh, Yufei Hu, Junli Cao, Xingjian Yan, Yi Xiao, Yong Lu, Jianming Liang, Demetri Terzopoulos, Xiaowei Ding

We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data.

Colorization Transfer Learning

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