Search Results for author: Qi Dou

Found 59 papers, 31 papers with code

TsmoBN: Interventional Generalization for Unseen Clients in Federated Learning

no code implementations19 Oct 2021 Meirui Jiang, Xiaofei Zhang, Michael Kamp, Xiaoxiao Li, Qi Dou

Generalizing federated learning (FL) models to unseen clients with non-iid data is a crucial topic, yet unsolved so far.

Domain Generalization Federated Learning +1

Stereo Dense Scene Reconstruction and Accurate Laparoscope Localization for Learning-Based Navigation in Robot-Assisted Surgery

no code implementations8 Oct 2021 Ruofeng Wei, Bin Li, Hangjie Mo, Bo Lu, Yonghao Long, Bohan Yang, Qi Dou, Yunhui Liu, Dong Sun

Then, we develop a dense visual reconstruction algorithm to represent the scene by surfels, estimate the laparoscope pose and fuse the depth data into a unified reference coordinate for tissue reconstruction.

Depth Estimation

Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling

1 code implementation19 Sep 2021 Cheng Chen, Quande Liu, Yueming Jin, Qi Dou, Pheng-Ann Heng

We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels.

Denoising Semantic Segmentation +1

SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning

1 code implementation30 Aug 2021 Jiaqi Xu, Bin Li, Bo Lu, Yun-hui Liu, Qi Dou, Pheng-Ann Heng

Ten learning-based surgical tasks are built in the platform, which are common in the real autonomous surgical execution.

Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks

no code implementations19 Aug 2021 Jiaze Wang, Kai Chen, Qi Dou

Furthermore, we design a recurrent reconstruction network for iterative residual refinement to progressively improve the reconstruction and correspondence estimations from coarse to fine.

6D Pose Estimation 6D Pose Estimation using RGB +1

Accurate Grid Keypoint Learning for Efficient Video Prediction

1 code implementation28 Jul 2021 Xiaojie Gao, Yueming Jin, Qi Dou, Chi-Wing Fu, Pheng-Ann Heng

Video prediction methods generally consume substantial computing resources in training and deployment, among which keypoint-based approaches show promising improvement in efficiency by simplifying dense image prediction to light keypoint prediction.

Video Prediction

E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception

no code implementations1 Jul 2021 Yonghao Long, Zhaoshuo Li, Chi Hang Yee, Chi Fai Ng, Russell H. Taylor, Mathias Unberath, Qi Dou

After that, a dynamic reconstruction algorithm which can estimate the tissue deformation and camera movement, and aggregate the information over time is proposed for surgical scene reconstruction.

Depth Estimation

Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

1 code implementation16 Jun 2021 Quande Liu, Hongzheng Yang, Qi Dou, Pheng-Ann Heng

This paper studies a practical yet challenging FL problem, named \textit{Federated Semi-supervised Learning} (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i. e., hospitals).

Classification Federated Learning +1

Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation

no code implementations5 Apr 2021 Cheng Xue, Qiao Deng, Xiaomeng Li, Qi Dou, Pheng Ann Heng

To deal with the high inter-rater variability, the study of imperfect label has great significance in medical image segmentation tasks.

Medical Image Segmentation

Temporal Memory Relation Network for Workflow Recognition from Surgical Video

1 code implementation30 Mar 2021 Yueming Jin, Yonghao Long, Cheng Chen, Zixu Zhao, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features.

One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video

no code implementations24 Mar 2021 Zixu Zhao, Yueming Jin, Bo Lu, Chi-Fai Ng, Qi Dou, Yun-hui Liu, Pheng-Ann Heng

To greatly increase the label efficiency, we explore a new problem, i. e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame.

Meta-Learning

Future Frame Prediction for Robot-assisted Surgery

no code implementations18 Mar 2021 Xiaojie Gao, Yueming Jin, Zixu Zhao, Qi Dou, Pheng-Ann Heng

Predicting future frames for robotic surgical video is an interesting, important yet extremely challenging problem, given that the operative tasks may have complex dynamics.

Future prediction Optical Flow Estimation

Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer

1 code implementation17 Mar 2021 Xiaojie Gao, Yueming Jin, Yonghao Long, Qi Dou, Pheng-Ann Heng

In this paper, we introduce, for the first time in surgical workflow analysis, Transformer to reconsider the ignored complementary effects of spatial and temporal features for accurate surgical phase recognition.

Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment

no code implementations6 Mar 2021 Xueying Shi, Yueming Jin, Qi Dou, Jing Qin, Pheng-Ann Heng

In this paper, we propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i. e., both kinematic and visual data, from simulator to real robot.

Gesture Recognition Surgical Gesture Recognition +1

FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

1 code implementation ICLR 2021 Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou

The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.

Autonomous Driving Federated Learning

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

no code implementations ICCV 2021 Kai Chen, Qi Dou

The prior adaptation intrinsically associates the adopted prior with different objects, from which we can accurately reconstruct the 3D canonical model of the specific object for pose estimation.

6D Pose Estimation using RGB

Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification

1 code implementation15 Sep 2020 Zhao Wang, Quande Liu, Qi Dou

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries.

COVID-19 Diagnosis General Classification

Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video

1 code implementation6 Jul 2020 Zixu Zhao, Yueming Jin, Xiaojie Gao, Qi Dou, Pheng-Ann Heng

Considering the fast instrument motion, we further introduce a flow compensator to estimate intermediate motion within continuous frames, with a novel cycle learning strategy.

Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains

1 code implementation4 Jul 2020 Quande Liu, Qi Dou, Pheng-Ann Heng

We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation.

Domain Generalization Meta-Learning +1

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

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.

Classification General Classification +1

LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition

1 code implementation21 Apr 2020 Xueying Shi, Yueming Jin, Qi Dou, Pheng-Ann Heng

Specifically, we propose a non-local recurrent convolutional network (NL-RCNet), which introduces non-local block to capture the long-range temporal dependency (LRTD) among continuous frames.

Active Learning

Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation

no code implementations8 Apr 2020 Youyi Song, Lei Zhu, Baiying Lei, Bin Sheng, Qi Dou, Jing Qin, Kup-Sze Choi

In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump.

Harmonizing Transferability and Discriminability for Adapting Object Detectors

1 code implementation CVPR 2020 Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, Qi Dou

Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection pipeline.

Weakly Supervised Object Detection

Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

1 code implementation22 Feb 2020 Cheng Chen, Qi Dou, Yueming Jin, Hao Chen, Jing Qin, Pheng-Ann Heng

We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities.

Brain Tumor Segmentation Tumor Segmentation

Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search

no code implementations20 Feb 2020 Xiaojie Gao, Yueming Jin, Qi Dou, Pheng-Ann Heng

Automatic surgical gesture recognition is fundamental for improving intelligence in robot-assisted surgery, such as conducting complicated tasks of surgery surveillance and skill evaluation.

Gesture Recognition Surgical Gesture Recognition

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

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

1 code implementation6 Feb 2020 Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng Ann Heng

In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain.

Medical Image Segmentation Unsupervised Domain Adaptation

Unpaired Multi-modal Segmentation via Knowledge Distillation

1 code implementation6 Jan 2020 Qi Dou, Quande Liu, Pheng Ann Heng, Ben Glocker

We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy.

Knowledge Distillation Semantic Segmentation

Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

no code implementations10 Oct 2019 Ben Glocker, Robert Robinson, Daniel C. Castro, Qi Dou, Ender Konukoglu

This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data.

IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation

no code implementations19 Aug 2019 Yanning Zhou, Hao Chen, Jiaqi Xu, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction.

Cell Segmentation Instance Segmentation +1

Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video

1 code implementation18 Jul 2019 Yueming Jin, Keyun Cheng, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel framework to leverage instrument motion information, by incorporating a derived temporal prior to an attention pyramid network for accurate segmentation.

Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis

1 code implementation13 Jul 2019 Yueming Jin, Huaxia Li, Qi Dou, Hao Chen, Jing Qin, Chi-Wing Fu, Pheng-Ann Heng

Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other.

Surgical tool detection

Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training

no code implementations24 Jun 2019 Wenhao Jiang, Zhiyu Liu, Kit-Hang Lee, Shihui Chen, Yui-Lun Ng, Qi Dou, Hing-Chiu Chang, Ka-Wai Kwok

Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis.

Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis

no code implementations7 Jun 2019 Luyang Luo, Hao Chen, Xi Wang, Qi Dou, Huangjin Lin, Juan Zhou, Gongjie Li, Pheng-Ann Heng

In this paper, we propose to identify breast tumor in MRI by Cosine Margin Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion by COrrelation Attention Map (COAM) based on the learned features.

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

3 code implementations5 Jun 2019 Martin Zlocha, Qi Dou, Ben Glocker

We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging.

Computed Tomography (CT) Region Proposal +1

CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation

no code implementations13 Mar 2019 Yanning Zhou, Omer Fahri Onder, Qi Dou, Efstratios Tsougenis, Hao Chen, Pheng-Ann Heng

Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment.

Instance Segmentation Multi-tissue Nucleus Segmentation +1

Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification

no code implementations23 Jan 2019 Cheng Xue, Qi Dou, Xueying Shi, Hao Chen, Pheng Ann Heng

In this paper, we propose an effective iterative learning framework for noisy-labeled medical image classification, to combat the lacking of high quality annotated medical data.

Classification General Classification +3

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

2 code implementations19 Dec 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng-Ann Heng

In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.

Cardiac Segmentation Domain Adaptation +1

3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

2 code implementations27 Jun 2018 Yi-Jie Huang, Qi Dou, Zi-Xian Wang, Li-Zhi Liu, Ying Jin, Chao-Feng Li, Lisheng Wang, Hao Chen, Rui-Hua Xu

With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detailpreserving segmentation and therefore enlarged applicable volume size and effective receptive field.

Multi-Task Learning Tumor Segmentation

MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images

no code implementations5 Jun 2018 Simon Graham, Hao Chen, Jevgenij Gamper, Qi Dou, Pheng-Ann Heng, David Snead, Yee Wah Tsang, Nasir Rajpoot

However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures.

Colorectal Gland Segmentation: Decision Making +3

Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation

no code implementations2 Jun 2018 Cheng Chen, Qi Dou, Hao Chen, Pheng-Ann Heng

In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being applied to new test datasets with domain shift.

Transfer Learning Unsupervised Domain Adaptation

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

2 code implementations29 Apr 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng

The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions.

Transfer Learning Unsupervised Domain Adaptation

SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction

no code implementations22 Dec 2017 Yanning Zhou, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng

Cell nuclei detection and fine-grained classification have been fundamental yet challenging problems in histopathology image analysis.

Classification General Classification

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

1 code implementation21 Sep 2017 Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, Pheng Ann Heng

Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

Automatic Liver And Tumor Segmentation Lesion Segmentation +2

Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning

no code implementations13 Aug 2017 Qi Dou, Hao Chen, Yueming Jin, Huangjing Lin, Jing Qin, Pheng-Ann Heng

In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment.

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.

ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images

no code implementations30 Jul 2017 Huangjing Lin, Hao Chen, Qi Dou, Liansheng Wang, Jing Qin, Pheng-Ann Heng

Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists.

Breast Cancer Detection whole slide images

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 Object Detection +1

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

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