Search Results for author: Pheng-Ann Heng

Found 110 papers, 48 papers with code

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 Representation Learning

Acknowledging the Unknown for Multi-label Learning with Single Positive Labels

no code implementations30 Mar 2022 Donghao Zhou, Pengfei Chen, Qiong Wang, Guangyong Chen, Pheng-Ann Heng

Due to the difficulty of collecting exhaustive multi-label annotations, multi-label training data often contains partial labels.

Multi-Label Learning

Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation

no code implementations29 Mar 2022 Yueming Jin, Yang Yu, Cheng Chen, Zixu Zhao, Pheng-Ann Heng, Danail Stoyanov

Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre.

Contrastive Learning Scene Segmentation

Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification

no code implementations18 Mar 2022 Luyang Luo, Dunyuan Xu, Hao Chen, Tien-Tsin Wong, Pheng-Ann Heng

Deep learning models were frequently reported to learn from shortcuts like dataset biases.

TraSeTR: Track-to-Segment Transformer with Contrastive Query for Instance-level Instrument Segmentation in Robotic Surgery

no code implementations17 Feb 2022 Zixu Zhao, Yueming Jin, Pheng-Ann Heng

Specifically, we introduce the prior query that encoded with previous temporal knowledge, to transfer tracking signals to current instances via identity matching.

Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network

1 code implementation8 Nov 2021 Jiacheng Wang, Yueming Jin, Shuntian Cai, Hongzhi Xu, Pheng-Ann Heng, Jing Qin, Liansheng Wang

Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks.

Multi-Task Learning

Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention

no code implementations5 Nov 2021 Mian Wu, Yinling Qian, Xiangyun Liao, Qiong Wang, Pheng-Ann Heng

In practice, we introduce the voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels, and adopt multi-scale convolutional operators to gain local spatial information.

Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning

1 code implementation NeurIPS 2021 Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, Pheng-Ann Heng

The backpropagation networks are notably susceptible to catastrophic forgetting, where networks tend to forget previously learned skills upon learning new ones.

Continual Learning

Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video

1 code implementation28 Sep 2021 Jiacheng Wang, Yueming Jin, Liansheng Wang, Shuntian Cai, Pheng-Ann Heng, Jing Qin

On the other hand, we develop an active global memory to gather the global semantic correlation in long temporal range to current one, in which we gather the most informative frames derived from model uncertainty and frame similarity.

Frame Optical Flow 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

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

no code implementations13 Sep 2021 Yijun Yang, Shujun Wang, Pheng-Ann Heng, Lequan Yu

In this paper, we present a novel Hierarchical Consistency framework for Domain Generalization (HCDG) by ensembling Extrinsic Consistency and Intrinsic Consistency.

Data Augmentation Domain Generalization +2

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.


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.

Frame Video Prediction

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).

Federated Learning Image Classification

Point Cloud Upsampling via Disentangled Refinement

2 code implementations CVPR 2021 Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy.

Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs

no code implementations21 Apr 2021 Luyang Luo, Hao Chen, Yongjie Xiao, Yanning Zhou, Xi Wang, Varut Vardhanabhuti, Mingxiang Wu, Pheng-Ann Heng

Then, we compared the models' internal performance on the lesion localization task and showed that CheXDet achieved significantly better performance than CheXNet even when given 80% less training data.

Classification Decision Making +3

Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection

no code implementations7 Apr 2021 Zhizhong Chai, Luyang Luo, Huangjing Lin, Hao Chen, Anjia Han, Pheng-Ann Heng

Specifically, our model learns a metric space and conducts dual alignment of semantic features on both the proposal level and the prototype levels.

Metric Learning Object Detection

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.

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.

Frame Future prediction +1

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

Deep Texture-Aware Features for Camouflaged Object Detection

no code implementations5 Feb 2021 Jingjing Ren, Xiaowei Hu, Lei Zhu, Xuemiao Xu, Yangyang Xu, Weiming Wang, Zijun Deng, Pheng-Ann Heng

Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings.

Object Detection

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 +1

C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing

no code implementations ICCV 2021 Yanning Zhou, Hang Xu, Wei zhang, Bin Gao, Pheng-Ann Heng

The semi-supervised semantic segmentation methods utilize the unlabeled data to increase the feature discriminative ability to alleviate the burden of the annotated data.

Contrastive Learning Data Augmentation +1

Noise against noise: stochastic label noise helps combat inherent label noise

no code implementations ICLR 2021 Pengfei Chen, Guangyong Chen, Junjie Ye, Jingwei Zhao, Pheng-Ann Heng

The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect, previously studied in optimization by analyzing the dynamics of parameter updates.

Learning with noisy labels

Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

1 code implementation10 Dec 2020 Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao, Pheng-Ann Heng

In this work, we present a theoretical hypothesis testing and prove that noise in real-world dataset is unlikely to be CCN, which confirms that label noise should depend on the instance and justifies the urgent need to go beyond the CCN assumption. The theoretical results motivate us to study the more general and practical-relevant instance-dependent noise (IDN).

Image Classification

Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

1 code implementation8 Dec 2020 Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao, Pheng-Ann Heng

For validation, we prove that a noisy validation set is reliable, addressing the critical demand of model selection in scenarios like hyperparameter-tuning and early stopping.

Learning with noisy labels Model Selection +1

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

no code implementations13 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 Semantic Segmentation

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 Knowledge Distillation +1

Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation

1 code implementation21 Jul 2020 Yanning Zhou, Hao Chen, Huangjing Lin, Pheng-Ann Heng

The teacher's self-ensemble predictions from $K$-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student.

Instance Segmentation Knowledge Distillation +1

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.

Domain Generalization Metric Learning +2

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

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

Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes

1 code implementation28 Apr 2020 Xin Yang, Xu Wang, Yi Wang, Haoran Dou, Shengli Li, Huaxuan Wen, Yi Lin, Pheng-Ann Heng, Dong Ni

In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes.

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

A Rotation-Invariant Framework for Deep Point Cloud Analysis

1 code implementation16 Mar 2020 Xianzhi Li, Ruihui Li, Guangyong Chen, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations.

Point Cloud Generation

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

1 code implementation CVPR 2020 Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu

We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network.

3D Point Cloud Data Augmentation Classification +2

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 Disentanglement +1

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.

Action Segmentation Frame +3

Instance Shadow Detection

2 code implementations CVPR 2020 Tianyu Wang, Xiao-Wei Hu, Qiong Wang, Pheng-Ann Heng, Chi-Wing Fu

Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results.

Instance Shadow Detection Shadow Detection

Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

2 code implementations16 Nov 2019 Xiaowei Hu, Tianyu Wang, Chi-Wing Fu, Yitong Jiang, Qiong Wang, Pheng-Ann Heng

Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world.

Shadow Detection

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.

CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images

no code implementations3 Sep 2019 Yanning Zhou, Simon Graham, Navid Alemi Koohbanani, Muhammad Shaban, Pheng-Ann Heng, Nasir Rajpoot

Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity.

Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes

no code implementations31 Aug 2019 Xu Wang, Xin Yang, Haoran Dou, Shengli Li, Pheng-Ann Heng, Dong Ni

In this paper, we propose an effective framework for simultaneous segmentation and landmark localization in prenatal ultrasound volumes.

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

Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening

no code implementations26 Jul 2019 Xi Wang, Hao Chen, Luyang Luo, An-ran Ran, Poemen P. Chan, Clement C. Tham, Carol Y. Cheung, Pheng-Ann Heng

Besides, the proposed multi-task learning network is capable of exploring the structure and function relationship from the OCT image and visual field measurement simultaneously, which contributes to classification performance boosting.

Multi-Task Learning

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.


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

5 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.

Left Atrium Segmentation Medical Image Segmentation +1

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

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 +3

Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

1 code implementation3 Jul 2019 Yi Wang, Haoran Dou, Xiao-Wei Hu, Lei Zhu, Xin Yang, Ming Xu, Jing Qin, Pheng-Ann Heng, Tianfu Wang, Dong Ni

Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers.

Medical Image Segmentation Semantic Segmentation

Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration

no code implementations3 Jul 2019 Lihao Liu, Xiaowei Hu, Lei Zhu, Pheng-Ann Heng

This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image.

Image Registration

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.

Frame Semantic Segmentation +1

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.

Unsupervised Detection of Distinctive Regions on 3D Shapes

no code implementations5 May 2019 Xianzhi Li, Lequan Yu, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

This paper presents a novel approach to learn and detect distinctive regions on 3D shapes.

PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis

no code implementations3 May 2019 Zixu Zhao, Huangjing Lin, Hao Chen, Pheng-Ann Heng

Automatic detection of cancer metastasis from whole slide images (WSIs) is a crucial step for following patient staging and prognosis.

whole slide images

Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data

4 code implementations ICCV 2019 Xiaowei Hu, Yitong Jiang, Chi-Wing Fu, Pheng-Ann Heng

This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples.

Shadow Removal

SAC-Net: Spatial Attenuation Context for Salient Object Detection

no code implementations25 Mar 2019 Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Tianyu Wang, Pheng-Ann Heng

This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects.

RGB Salient Object Detection Salient Object Detection

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

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

1 code implementation28 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.

Lesion Segmentation Liver Segmentation +4

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.

Unsupervised Domain Adaptation

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

Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection

1 code implementation ECCV 2018 Lei Zhu, Zijun Deng, Xiao-Wei Hu, Chi-Wing Fu, Xuemiao Xu, Jing Qin, Pheng-Ann Heng

Second, we develop a bidirectional feature pyramid network (BFPN) to aggregate shadow contexts spanned across different CNN layers by deploying two series of RAR modules in the network to iteratively combine and refine context features: one series to refine context features from deep to shallow layers, and another series from shallow to deep layers.

Shadow Detection

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 Skin Lesion Segmentation

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 Skin Lesion 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

Direction-aware Spatial Context Features for Shadow Detection and Removal

1 code implementation12 May 2018 Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Jing Qin, Pheng-Ann Heng

This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.

Shadow Detection And Removal Shadow Removal

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

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

no code implementations2 Apr 2018 Xiaowei Hu, Xuemiao Xu, Yongjie Xiao, Hao Chen, Shengfeng He, Jing Qin, Pheng-Ann Heng

Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales.

Fast Vehicle Detection Object Detection

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

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.

General Classification

Direction-aware Spatial Context Features for Shadow Detection

1 code implementation CVPR 2018 Xiaowei Hu, Lei Zhu, Chi-Wing Fu, Jing Qin, Pheng-Ann Heng

To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN.

Detecting Shadows Shadow Detection

Joint Bi-Layer Optimization for Single-Image Rain Streak Removal

no code implementations ICCV 2017 Lei Zhu, Chi-Wing Fu, Dani Lischinski, Pheng-Ann Heng

A third prior is defined on the rain-streak layer R, based on similarity of patches to the extracted rain patches.

Rain Removal

Online Robust Image Alignment via Subspace Learning From Gradient Orientations

no code implementations ICCV 2017 Qingqing Zheng, Yi Wang, Pheng-Ann Heng

The proposed method integrates the subspace learning, transformed IGO reconstruction and image alignment into a unified online framework, which is robust for aligning images with severe intensity distortions.

Face Recognition

Cascaded Feature Network for Semantic Segmentation of RGB-D Images

no code implementations ICCV 2017 Di Lin, Guangyong Chen, Daniel Cohen-Or, Pheng-Ann Heng, Hui Huang

Our approach is to use the available depth to split the image into layers with common visual characteristic of objects/scenes, or common "scene-resolution".

Semantic Segmentation

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

A Non-Local Low-Rank Framework for Ultrasound Speckle Reduction

no code implementations CVPR 2017 Lei Zhu, Chi-Wing Fu, Michael S. Brown, Pheng-Ann Heng

`Speckle' refers to the granular patterns that occur in ultrasound images due to wave interference.

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.

Medical Image Segmentation Semantic Segmentation

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

Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images

no code implementations7 Jul 2016 Hao Chen, Yefeng Zheng, Jin-Hyeong Park, Pheng-Ann Heng, S. Kevin Zhou

Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements.

Transfer Learning

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

From Noise Modeling to Blind Image Denoising

no code implementations CVPR 2016 Fengyuan Zhu, Guangyong Chen, Pheng-Ann Heng

This paper addresses this problem and proposes a novel blind image denoising algorithm which can cope with real-world noisy images even when the noise model is not provided.

Image Denoising

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

no code implementations CVPR 2016 Hao Chen, Xiaojuan Qi, Lequan Yu, Pheng-Ann Heng

The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas.

Multi-Task Learning

Blind Image Denoising via Dependent Dirichlet Process Tree

no code implementations13 Jan 2016 Fengyuan Zhu, Guangyong Chen, Jianye Hao, Pheng-Ann Heng

This paper addresses this problem and proposes a novel blind image denoising algorithm to recover the clean image from noisy one with the unknown noise model.

Image Denoising Variational Inference

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