Search Results for author: Lequan Yu

Found 63 papers, 31 papers with code

Single-Shot Plug-and-Play Methods for Inverse Problems

no code implementations22 Nov 2023 Yanqi Cheng, Lipei Zhang, Zhenda Shen, Shujun Wang, Lequan Yu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero

In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.

MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction

no code implementations11 Nov 2023 Yan Miao, Lequan Yu

Hospital readmission prediction is considered an essential approach to decreasing readmission rates, which is a key factor in assessing the quality and efficacy of a healthcare system.

Readmission Prediction

FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound

no code implementations30 Oct 2023 Chaoyu Chen, Xin Yang, Yuhao Huang, Wenlong Shi, Yan Cao, Mingyuan Luo, Xindi Hu, Lei Zhue, Lequan Yu, Kejuan Yue, Yuanji Zhang, Yi Xiong, Dong Ni, Weijun Huang

However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses.

Pose Estimation Self-Supervised Learning

HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis

1 code implementation14 Sep 2023 Ziyu Guo, Weiqin Zhao, Shujun Wang, Lequan Yu

Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids.

whole slide images

Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars

no code implementations27 Aug 2023 Weijia Feng, Lingting Zhu, Lequan Yu

However, the adoption of foundational models in the medical domain presents a challenge due to the difficulty and expense of labeling sufficient data for adaptation within hospital systems.

Brain Tumor Segmentation Image Segmentation +2

ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis

1 code implementation ICCV 2023 Yanyan Huang, Weiqin Zhao, Shujun Wang, Yu Fu, Yuming Jiang, Lequan Yu

In this paper, we propose the FIRST continual learning framework for WSI analysis, named ConSlide, to tackle the challenges of enormous image size, utilization of hierarchical structure, and catastrophic forgetting by progressive model updating on multiple sequential datasets.

Continual Learning

Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation

1 code implementation21 Jul 2023 Qingyue Wei, Lequan Yu, Xianhang Li, Wei Shao, Cihang Xie, Lei Xing, Yuyin Zhou

Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data.

Image Segmentation Meta-Learning +4

Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis

no code implementations19 Jul 2023 Lingting Zhu, Zeyue Xue, Zhenchao Jin, Xian Liu, Jingzhen He, Ziwei Liu, Lequan Yu

This paradigm extends the 2D image diffusion model to a volumetric version with a slightly increasing number of parameters and computation, offering a principled solution for generic cross-modality 3D medical image synthesis.

Image Generation

Relabeling Minimal Training Subset to Flip a Prediction

no code implementations22 May 2023 Jinghan Yang, Linjie Xu, Lequan Yu

When facing an unsatisfactory prediction from a machine learning model, it is crucial to investigate the underlying reasons and explore the potential for reversing the outcome.

HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation

1 code implementation18 Mar 2023 Zhaohu Xing, Lei Zhu, Lequan Yu, Zhiheng Xing, Liang Wan

Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique.

Contrastive Learning Image Segmentation +3

Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation

1 code implementation CVPR 2023 Lingting Zhu, Xian Liu, Xuanyu Liu, Rui Qian, Ziwei Liu, Lequan Yu

In this work, we propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture), to effectively capture the cross-modal audio-to-gesture associations and preserve temporal coherence for high-fidelity audio-driven co-speech gesture generation.

Gesture Generation

MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

1 code implementation CVPR 2023 Duowen Chen, Yunhao Bai, Wei Shen, Qingli Li, Lequan Yu, Yan Wang

Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch).

Anatomy Data Augmentation +4

Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening

no code implementations9 Nov 2022 Kang Li, Lequan Yu, Pheng-Ann Heng

Particularly, we first present a style-oriented replay module to enable structure-realistic and memory-efficient reproduction of past data, and then incorporate the replayed past data to jointly optimize the model with current data to alleviate catastrophic forgetting.

Image Segmentation Incremental Learning +2

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

2 code implementations12 Oct 2022 Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, Lequan Yu

In this paper, we present a novel Multi-Granularity Cross-modal Alignment (MGCA) framework for generalized medical visual representation learning by harnessing the naturally exhibited semantic correspondences between medical image and radiology reports at three different levels, i. e., pathological region-level, instance-level, and disease-level.

Contrastive Learning Image Classification +4

MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation

2 code implementations9 Sep 2022 Zhenchao Jin, Dongdong Yu, Zehuan Yuan, Lequan Yu

To this end, we propose a novel soft mining contextual information beyond image paradigm named MCIBI++ to further boost the pixel-level representations.

Segmentation Semantic Segmentation +1

Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning

1 code implementation4 Sep 2022 Tianling Liu, Wennan Liu, Lequan Yu, Liang Wan, Tong Han, Lei Zhu

Preoperative and noninvasive prediction of the meningioma grade is important in clinical practice, as it directly influences the clinical decision making.

Contrastive Learning Decision Making +1

NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation

1 code implementation31 Aug 2022 Zhaohu Xing, Lequan Yu, Liang Wan, Tong Han, Lei Zhu

Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information.

Brain Tumor Segmentation MRI segmentation +2

You Should Look at All Objects

1 code implementation16 Jul 2022 Zhenchao Jin, Dongdong Yu, Luchuan Song, Zehuan Yuan, Lequan Yu

Feature pyramid network (FPN) is one of the key components for object detectors.

Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training

no code implementations10 May 2022 Cheng Xue, Lequan Yu, Pengfei Chen, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data.

Image Classification Medical Image Classification +1

CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

no code implementations8 Apr 2022 Yiqing Shen, Yuyin Zhou, Lequan Yu

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.

Federated Learning

Learning to Bootstrap for Combating Label Noise

1 code implementation9 Feb 2022 Yuyin Zhou, Xianhang Li, Fengze Liu, Xuxi Chen, Lequan Yu, Cihang Xie, Matthew P. Lungren, Lei Xing

Specifically, our method dynamically adjusts the per-sample importance weight between the real observed labels and pseudo-labels, where the weights are efficiently determined in a meta process.

Ranked #8 on Image Classification on Clothing1M (using clean data) (using extra training data)

Image Classification Representation Learning

CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning

no code implementations CVPR 2022 Yiqing Shen, Yuyin Zhou, Lequan Yu

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.

Federated Learning

Rethinking Client Reweighting for Selfish Federated Learning

no code implementations29 Sep 2021 Ruichen Luo, Shoubo Hu, Lequan Yu

To this end, we study a new $\textit{selfish}$ variant of federated learning, in which the ultimate objective is to learn a model with optimal performance on internal clients $\textit{alone}$ instead of all clients.

Federated Learning Image Classification +4

Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning

no code implementations28 Sep 2021 Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Hongyi Ren, Wei Zhao, Lei Xing

We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images.

Image Reconstruction Metal Artifact Reduction

All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

1 code implementation28 Sep 2021 Zhe Xu, Yixin Wang, Donghuan Lu, Lequan Yu, Jiangpeng Yan, Jie Luo, Kai Ma, Yefeng Zheng, Raymond Kai-yu Tong

Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?

Brain Tumor Segmentation Image Segmentation +3

HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation

1 code implementation13 Sep 2021 Yijun Yang, Shujun Wang, Lei Zhu, Lequan Yu

Particularly, for the Extrinsic Consistency, we leverage the knowledge across multiple source domains to enforce data-level consistency.

Data Augmentation Domain Generalization +4

nnFormer: Interleaved Transformer for Volumetric Segmentation

2 code implementations7 Sep 2021 Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.

Image Segmentation Inductive Bias +3

CateNorm: Categorical Normalization for Robust Medical Image Segmentation

1 code implementation29 Mar 2021 Junfei Xiao, Lequan Yu, Zongwei Zhou, Yutong Bai, Lei Xing, Alan Yuille, Yuyin Zhou

We propose a new normalization strategy, named categorical normalization (CateNorm), to normalize the activations according to categorical statistics.

Image Segmentation Medical Image Segmentation +2

TransCT: Dual-path Transformer for Low Dose Computed Tomography

1 code implementation28 Feb 2021 Zhicheng Zhang, Lequan Yu, Xiaokun Liang, Wei Zhao, Lei Xing

Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients.

Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation

1 code implementation7 Jan 2021 Kang Li, Shujun Wang, Lequan Yu, Pheng-Ann Heng

In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation.

Cardiac Segmentation Domain Adaptation +2

DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets

1 code implementation13 Oct 2020 Shujun Wang, Lequan Yu, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng

Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative.

Domain Generalization Image Segmentation +2

Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation

no code implementations4 Oct 2020 Kang Li, Lequan Yu, Shujun Wang, Pheng-Ann Heng

Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e. g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity.

Cardiac Segmentation Image Segmentation +3

Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images

no code implementations16 Sep 2020 Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Lei Xing

Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance.

Computed Tomography (CT) Image Generation +2

Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis

1 code implementation21 Jul 2020 Xiaomeng Li, Mengyu Jia, Md Tauhidul Islam, Lequan Yu, Lei Xing

The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making.

Decision Making

Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization

no code implementations ECCV 2020 Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, Pheng-Ann Heng

To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains.

Anomaly Detection Domain Generalization +3

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

no code implementations28 Jun 2020 Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.

Image Segmentation Organ Segmentation +6

Deep Mining External Imperfect Data for Chest X-ray Disease Screening

no code implementations6 Jun 2020 Luyang Luo, Lequan Yu, Hao Chen, Quande Liu, Xi Wang, Jiaqi Xu, Pheng-Ann Heng

Recent researches have demonstrated that performance bottleneck exists in joint training on different CXR datasets, and few made efforts to address the obstacle.

General Classification Thoracic Disease Classification

Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model

1 code implementation15 May 2020 Quande Liu, Lequan Yu, Luyang Luo, Qi Dou, Pheng Ann Heng

It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.

General Classification Multi-Label Image Classification +1

MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data

2 code implementations9 Feb 2020 Quande Liu, Qi Dou, Lequan Yu, Pheng Ann Heng

However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training.

Transfer Learning

CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

1 code implementation4 Nov 2019 Xiaomeng Li, Xiao-Wei Hu, Lequan Yu, Lei Zhu, Chi-Wing Fu, Pheng-Ann Heng

In this paper, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision.

Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound

1 code implementation10 Oct 2019 Haoran Dou, Xin Yang, Jikuan Qian, Wufeng Xue, Hao Qin, Xu Wang, Lequan Yu, Shujun Wang, Yi Xiong, Pheng-Ann Heng, Dong Ni

In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US.

Reinforcement Learning (RL)

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

7 code implementations16 Jul 2019 Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, Pheng-Ann Heng

We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.

Image Segmentation Left Atrium Segmentation +3

Revisiting Metric Learning for Few-Shot Image Classification

no code implementations6 Jul 2019 Xiaomeng Li, Lequan Yu, Chi-Wing Fu, Meng Fang, Pheng-Ann Heng

However, the importance of feature embedding, i. e., exploring the relationship among training samples, is neglected.

Classification Few-Shot Image Classification +4

Difficulty-aware Meta-learning for Rare Disease Diagnosis

no code implementations30 Jun 2019 Xiaomeng Li, Lequan Yu, Yueming Jin, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng

Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data.

General Classification Lesion Classification +2

Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

1 code implementation26 Jun 2019 Shujun Wang, Lequan Yu, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng

The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets. In this work, we present an unsupervised domain adaptation framework, called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions.

Image Segmentation Segmentation +2

Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

no code implementations28 Feb 2019 Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng

In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.

Image Segmentation Lesion Segmentation +6

Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation

no code implementations20 Feb 2019 Shujun Wang, Lequan Yu, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng

In this paper, we present a novel patchbased Output Space Adversarial Learning framework (pOSAL) to jointly and robustly segment the OD and OC from different fundus image datasets.

Segmentation Unsupervised Domain Adaptation

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

no code implementations29 Nov 2018 Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth

Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.

Image Segmentation Medical Image Segmentation +2

Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model

no code implementations12 Aug 2018 Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Pheng-Ann Heng

In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.

Lesion Segmentation Segmentation +1

EC-Net: an Edge-aware Point set Consolidation Network

no code implementations ECCV 2018 Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds.

Surface Reconstruction

Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images

1 code implementation8 Jul 2018 Xiaomeng Li, Lequan Yu, Chi-Wing Fu, Pheng-Ann Heng

Our best model achieves 77. 23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.

Lesion Segmentation Segmentation +2

PU-Net: Point Cloud Upsampling Network

3 code implementations CVPR 2018 Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.

Point Cloud Super Resolution

Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

2 code implementations2 Aug 2017 Lequan Yu, Jie-Zhi Cheng, Qi Dou, Xin Yang, Hao Chen, Jing Qin, Pheng-Ann Heng

Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data.

Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images

no code implementations6 Dec 2016 Xin Yang, Lequan Yu, Lingyun Wu, Yi Wang, Dong Ni, Jing Qin, Pheng-Ann Heng

Additionally, our approach is general and can be extended to other medical image segmentation tasks, where boundary incompleteness is one of the main challenges.

Image Segmentation Medical Image Segmentation +1

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

3 code implementations21 Aug 2016 Hao Chen, Qi Dou, Lequan Yu, Pheng-Ann Heng

Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e. g., object detection and segmentation.

Brain Segmentation Image Segmentation +4

3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

no code implementations3 Jul 2016 Qi Dou, Hao Chen, Yueming Jin, Lequan Yu, Jing Qin, Pheng-Ann Heng

Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment.

Liver Segmentation Segmentation

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