Search Results for author: Shujun Wang

Found 27 papers, 9 papers with code

CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification

no code implementations25 Mar 2024 Guangqian Yang, Kangrui Du, Zhihan Yang, Ye Du, Yongping Zheng, Shujun Wang

Our proposed framework is built on a masked Vim autoencoder to learn a unified multi-modal representation and long-dependencies contained in 3D medical images.

Contrastive Learning Representation Learning

Anatomical Structure-Guided Medical Vision-Language Pre-training

no code implementations14 Mar 2024 Qingqiu Li, Xiaohan Yan, Jilan Xu, Runtian Yuan, Yuejie Zhang, Rui Feng, Quanli Shen, Xiaobo Zhang, Shujun Wang

For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample and constructing soft labels for contrastive learning to improve the semantic association of different image-report pairs.

Contrastive Learning Decoder +3

TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios

no code implementations30 Nov 2023 Lihao Liu, Yanqi Cheng, Zhongying Deng, Shujun Wang, Dongdong Chen, Xiaowei Hu, Pietro Liò, Carola-Bibiane Schönlieb, Angelica Aviles-Rivero

Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms.

Multi-Object Tracking Object

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.

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

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

HGIB: Prognosis for Alzheimer's Disease via Hypergraph Information Bottleneck

no code implementations18 Mar 2023 Shujun Wang, Angelica I Aviles-Rivero, Zoe Kourtzi, Carola-Bibiane Schönlieb

We demonstrate, through extensive experiments on ADNI, that our proposed HGIB framework outperforms existing state-of-the-art hypergraph neural networks for Alzheimer's disease prognosis.

GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue

no code implementations14 Mar 2023 Zhening Huang, Xiaoyang Wu, Hengshuang Zhao, Lei Zhu, Shujun Wang, Georgios Hadjidemetriou, Ioannis Brilakis

For feature aggregation, it improves feature modeling by allowing the network to learn from both local points and neighboring geometry partitions, resulting in an enlarged data-tailored receptive field.

Point Cloud Segmentation

TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation

no code implementations17 Nov 2022 Zhongying Deng, Yanqi Chen, Lihao Liu, Shujun Wang, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero

Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.

Instance Segmentation Management +1

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

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

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

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

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

8 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

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

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

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