Search Results for author: Yefeng Zheng

Found 104 papers, 39 papers with code

Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation

no code implementations ECCV 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Recent generative adversarial network (GAN) based methods (e. g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation.

Domain Adaptation Image-to-Image Translation +2

Dual Adversarial Network for Deep Active Learning

no code implementations ECCV 2020 Shuo Wang, Yuexiang Li, Kai Ma, Ruhui Ma, Haibing Guan, Yefeng Zheng

In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.

Active Learning

Robust Representation via Dynamic Feature Aggregation

1 code implementation16 May 2022 Haozhe Liu, Haoqin Ji, Yuexiang Li, Nanjun He, Haoqian Wu, Feng Liu, Linlin Shen, Yefeng Zheng

With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks.

OOD Detection

Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction

1 code implementation16 May 2022 Hong Wang, Yuexiang Li, Deyu Meng, Yefeng Zheng

By unfolding every iterative substep of the proposed algorithm into a network module, we explicitly embed the prior structure into a deep network, \emph{i. e.,} a clear interpretability for the MAR task.

Computed Tomography (CT) Metal Artifact Reduction

Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation

no code implementations23 Apr 2022 Cong Xie, Hualuo Liu, Shilei Cao, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

A cosine similarity based attention module is proposed to fuse the information from both encoders, to utilize both types of prior information encoded by the template-encoder and model the inter-subject similarity for each foreground class.

Semantic Segmentation

DFTR: Depth-supervised Fusion Transformer for Salient Object Detection

no code implementations12 Mar 2022 Heqin Zhu, Xu sun, Yuexiang Li, Kai Ma, S. Kevin Zhou, Yefeng Zheng

This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.

Object Detection Salient Object Detection

Deep Convolutional Neural Networks for Molecular Subtyping of Gliomas Using Magnetic Resonance Imaging

no code implementations10 Mar 2022 Dong Wei, Yiming Li, Yinyan Wang, Tianyi Qian, Yefeng Zheng

Methods: A DCNN model was developed for the prediction of the five glioma subtypes based on a hierarchical classification paradigm.

Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network

no code implementations7 Mar 2022 Shuxin Wang, Shilei Cao, Zhizhong Chai, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset.

Tumor Segmentation

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images

1 code implementation4 Mar 2022 Hong Liu, Dong Wei, Donghuan Lu, Yuexiang Li, Kai Ma, Liansheng Wang, Yefeng Zheng

To the best of our knowledge, this is the first study that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs.

Label Propagation for Annotation-Efficient Nuclei Segmentation from Pathology Images

no code implementations16 Feb 2022 Yi Lin, Zhiyong Qu, Hao Chen, Zhongke Gao, Yuexiang Li, Lili Xia, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng

Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H\&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm.

Representation Learning

A Survey of Cross-Modality Brain Image Synthesis

no code implementations14 Feb 2022 Guoyang Xie, Jinbao Wang, Yawen Huang, Yefeng Zheng, Feng Zheng, Yaochu Jin

The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in diagnosis of brain diseases.

Image Generation

FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss

no code implementations29 Jan 2022 Guoyang Xie, Jinbao Wang, Yawen Huang, Yefeng Zheng, Feng Zheng, Yaochu Jin

The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in the diagnosis of brain diseases.

Data Augmentation Image Generation +1

FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis

no code implementations22 Jan 2022 Guoyang Xie, Jinbao Wang, Yawen Huang, Yuexiang Li, Yefeng Zheng, Feng Zheng, Yaochu Jin

There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.

Federated Learning Image Generation +1

Revisiting Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship

no code implementations31 Dec 2021 Quanziang Wang, Yuexiang Li, Dong Wei, Renzhen Wang, Kai Ma, Yefeng Zheng, Deyu Meng

These approaches save a small part of the data of the past tasks as a memory buffer to prevent models from forgetting previously learned knowledge.

Continual Learning Meta-Learning

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

1 code implementation23 Dec 2021 Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Meng, Yefeng Zheng

During the computed tomography (CT) imaging process, metallic implants within patients always cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis.

Computed Tomography (CT) Metal Artifact Reduction

A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data

no code implementations18 Oct 2021 Hengji Cui, Dong Wei, Kai Ma, Shi Gu, Yefeng Zheng

In this work, we propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML).

Medical Image Segmentation Metric Learning +1

Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix

no code implementations17 Oct 2021 Ziqi Zhang, Yuexiang Li, Hongxin Wei, Kai Ma, Tao Xu, Yefeng Zheng

The hard samples, which are beneficial for classifier learning, are often mistakenly treated as noises in such a setting since both the hard samples and ones with noisy labels lead to a relatively larger loss value than the easy cases.

Image Classification

Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation

1 code implementation17 Oct 2021 Yinghuan Shi, Jian Zhang, Tong Ling, Jiwen Lu, Yefeng Zheng, Qian Yu, Lei Qi, Yang Gao

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.

Semantic Segmentation Semi-supervised Medical Image Segmentation

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

1 code implementation12 Oct 2021 Jiayuan Ding, Tong Xiang, Zijing Ou, Wangyang Zuo, Ruihui Zhao, Chenghua Lin, Yefeng Zheng, Bang Liu

In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query.

Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification

1 code implementation9 Oct 2021 Jinghan Sun, Dong Wei, Kai Ma, Liansheng Wang, Yefeng Zheng

Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases, composing a hybrid approach taking advantages of both unsupervised and (pseudo-) supervised learning on the base dataset.

Classification Few-Shot Learning +1

PAC-Bayes Information Bottleneck

1 code implementation ICLR 2022 Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu, Jimeng Sun, Xi Chen, Yefeng Zheng

Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.

Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views

1 code implementation24 Sep 2021 Dong Wei, Kai Ma, Yefeng Zheng

Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target view, for a globally optimal prescription.

InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction

1 code implementation11 Sep 2021 Hong Wang, Yuexiang Li, Haimiao Zhang, Jiawei Chen, Kai Ma, Deyu Meng, Yefeng Zheng

For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration.

Metal Artifact Reduction

A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework

no code implementations18 Aug 2021 Munan Ning, Cheng Bian, Dong Wei, Chenglang Yuan, Yaohua Wang, Yang Guo, Kai Ma, Yefeng Zheng

Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to another target domain, the model usually performs poorly.

Representation Learning Unsupervised Domain Adaptation

Multi-Anchor Active Domain Adaptation for Semantic Segmentation

1 code implementation ICCV 2021 Munan Ning, Donghuan Lu, Dong Wei, Cheng Bian, Chenglang Yuan, Shuang Yu, Kai Ma, Yefeng Zheng

Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation by aligning the synthetic source-domain data and the real-world target-domain samples.

Active Learning Domain Adaptation +1

CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse

no code implementations SEMEVAL 2021 Jiarun Cao, Yuejia Xiang, Yunyan Zhang, Zhiyuan Qi, Xi Chen, Yefeng Zheng

Accordingly, we propose CONNER, a cascade count and measurement extraction tool that can identify entities and the corresponding relations in a two-step pipeline model.

Joint Entity and Relation Extraction

RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation

no code implementations19 Jul 2021 Cong Xie, Shilei Cao, Dong Wei, HongYu Zhou, Kai Ma, Xianli Zhang, Buyue Qian, Liansheng Wang, Yefeng Zheng

Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance.

Computed Tomography (CT) Lesion Detection +1

Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration

no code implementations6 Jul 2021 Zhe Xu, Jie Luo, Donghuan Lu, Jiangpeng Yan, Sarah Frisken, Jayender Jagadeesan, William Wells III, Xiu Li, Yefeng Zheng, Raymond Tong

Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the "one value fits all" training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness.

Image Registration

Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint

no code implementations30 Jun 2021 Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng

In practical applications, the outdoor weather and illumination are changeable, e. g., cloudy and nighttime, which results in a significant drop of semantic segmentation accuracy of CNN only trained with daytime data.

Autonomous Driving Semantic Segmentation +2

Residual Moment Loss for Medical Image Segmentation

no code implementations27 Jun 2021 Quanziang Wang, Renzhen Wang, Yuexiang Li, Kai Ma, Yefeng Zheng, Deyu Meng

Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segmentation.

Medical Image Segmentation Semantic Segmentation

Calibrated RGB-D Salient Object Detection

1 code implementation CVPR 2021 Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, Li Cheng

Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).

RGB-D Salient Object Detection Salient Object Detection

Prototypical Graph Contrastive Learning

no code implementations17 Jun 2021 Shuai Lin, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, Xiaodan Liang

However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i. e., the negatives likely having the same semantic structure with the query, leading to performance degradation.

Contrastive Learning Representation Learning

PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System

1 code implementation16 Jun 2021 Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Yefeng Zheng

Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i. e., mappings) between two KGs.

LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction

no code implementations12 Jun 2021 Yi Lin, Yanfei Liu, Hao Chen, Xin Yang, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng

To the best of our knowledge, this is the first attempt to investigate NAS and knowledge distillation in ensemble learning, especially in the field of medical image analysis.

Ensemble Learning Knowledge Distillation +1

Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

no code implementations3 Jun 2021 Zhe Xu, Donghuan Lu, Yixin Wang, Jie Luo, Jayender Jagadeesan, Kai Ma, Yefeng Zheng, Xiu Li

Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data.

Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval

1 code implementation ACL 2021 Zijing Ou, Qinliang Su, Jianxing Yu, Bang Liu, Jingwen Wang, Ruihui Zhao, Changyou Chen, Yefeng Zheng

With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval.

Information Retrieval

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting

no code implementations ACL 2021 Yi Cheng, SiYao Li, Bang Liu, Ruihui Zhao, Sujian Li, Chenghua Lin, Yefeng Zheng

This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels.

Question Answering Question Generation

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

1 code implementation12 May 2021 Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Yuejia Xiang, Ningyu Zhang, Yefeng Zheng

Knowledge Graph (KG) alignment is to discover the mappings (i. e., equivalent entities, relations, and others) between two KGs.

Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management

1 code implementation NAACL 2021 Zhengxu Hou, Bang Liu, Ruihui Zhao, Zijing Ou, Yafei Liu, Xi Chen, Yefeng Zheng

For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs.

reinforcement-learning

Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

no code implementations30 Mar 2021 Hong-Yu Zhou, Hualuo Liu, Shilei Cao, Dong Wei, Chixiang Lu, Yizhou Yu, Kai Ma, Yefeng Zheng

In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic.

One-Shot Segmentation

Stabilized Medical Image Attacks

1 code implementation9 Mar 2021 Gege Qi, Lijun Gong, Yibing Song, Kai Ma, Yefeng Zheng

However, a threat to these systems arises that adversarial attacks make CNNs vulnerable.

Adversarial Attack Medical Diagnosis

Lifelong Learning based Disease Diagnosis on Clinical Notes

1 code implementation27 Feb 2021 Zifeng Wang, Yifan Yang, Rui Wen, Xi Chen, Shao-Lun Huang, Yefeng Zheng

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.

MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

1 code implementation26 Feb 2021 Luyan Liu, Zhiwei Wen, Songwei Liu, Hong-Yu Zhou, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma, Yefeng Zheng

Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.

Medical Image Segmentation Semantic Segmentation

Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation

no code implementations18 Jan 2021 Xiaoting Han, Lei Qi, Qian Yu, Ziqi Zhou, Yefeng Zheng, Yinghuan Shi, Yang Gao

These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images.

Medical Image Segmentation Semantic Segmentation +2

Stabilized Medical Attacks

no code implementations ICLR 2021 Gege Qi, Lijun Gong, Yibing Song, Kai Ma, Yefeng Zheng

We further analyze the KL-divergence of the proposed loss function and find that the loss stabilization term makes the perturbations updated towards a fixed objective spot while deviating from the ground truth.

Adversarial Attack Medical Diagnosis

Ensembled ResUnet for Anatomical Brain Barriers Segmentation

no code implementations29 Dec 2020 Munan Ning, Cheng Bian, Chenglang Yuan, Kai Ma, Yefeng Zheng

However, due to the visual and anatomical differences between different modalities, the accurate segmentation of brain structures becomes challenging.

A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks

1 code implementation17 Dec 2020 Qingsong Yao, Zecheng He, Yi Lin, Kai Ma, Yefeng Zheng, S. Kevin Zhou

Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making.

Adversarial Attack Decision Making

Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses

no code implementations8 Dec 2020 Yi Liu, Xingliang Yuan, Ruihui Zhao, Cong Wang, Dusit Niyato, Yefeng Zheng

Extensive case studies have shown that our attacks are effective on different datasets and common semi-supervised learning methods.

Federated Learning Quantization

An Industry Evaluation of Embedding-based Entity Alignment

1 code implementation COLING 2020 Ziheng Zhang, Jiaoyan Chen, Xi Chen, Hualuo Liu, Yuejia Xiang, Bo Liu, Yefeng Zheng

Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage.

Entity Alignment Knowledge Graphs

Finding Influential Instances for Distantly Supervised Relation Extraction

no code implementations17 Sep 2020 Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng

Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise.

Relation Extraction

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

1 code implementation NeurIPS 2020 Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.

Recommendation Systems

Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

no code implementations6 Sep 2020 Zifeng Wang, Rui Wen, Xi Chen, Shilei Cao, Shao-Lun Huang, Buyue Qian, Yefeng Zheng

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).

Graph Representation Learning

From Rain Generation to Rain Removal

1 code implementation CVPR 2021 Hong Wang, Zongsheng Yue, Qi Xie, Qian Zhao, Yefeng Zheng, Deyu Meng

For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets.

Single Image Deraining Variational Inference

Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling

no code implementations29 Jul 2020 Shuang Yu, Hong-Yu Zhou, Kai Ma, Cheng Bian, Chunyan Chu, Hanruo Liu, Yefeng Zheng

However, when being used for model training, only the final ground-truth label is utilized, while the critical information contained in the raw multi-rater gradings regarding the image being an easy/hard case is discarded.

Classification General Classification

TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification

no code implementations29 Jul 2020 Wenting Chen, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Chunyan Chu, Linlin Shen, Yefeng Zheng

A topology ranking discriminator based on ordinal regression is proposed to rank the topological connectivity level of the ground-truth, the generated A/V mask and the intentionally shuffled mask.

Classification General Classification

Learning Crisp Edge Detector Using Logical Refinement Network

no code implementations24 Jul 2020 Luyan Liu, Kai Ma, Yefeng Zheng

Edge detection is a fundamental problem in different computer vision tasks.

Edge Detection

MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint

no code implementations22 Jul 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models.

Domain Adaptation Translation

Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning

1 code implementation22 Jul 2020 Junde Wu, Shuang Yu, WenTing Chen, Kai Ma, Rao Fu, Hanruo Liu, Xiaoguang Di, Yefeng Zheng

Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts.

Classification General Classification +1

Instance-aware Self-supervised Learning for Nuclei Segmentation

no code implementations22 Jul 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.

Instance Segmentation Self-Supervised Learning +1

Deep Image Clustering with Category-Style Representation

1 code implementation ECCV 2020 Junjie Zhao, Donghuan Lu, Kai Ma, Yu Zhang, Yefeng Zheng

In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.

Deep Clustering Image Clustering

A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation

no code implementations20 Jul 2020 Munan Ning, Cheng Bian, Donghuan Lu, Hong-Yu Zhou, Shuang Yu, Chenglang Yuan, Yang Guo, Yaohua Wang, Kai Ma, Yefeng Zheng

Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.

Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation

no code implementations20 Jul 2020 Yuexiang Li, Jia-Wei Chen, Xinpeng Xie, Kai Ma, Yefeng Zheng

A novel pseudo-label (namely self-loop uncertainty), generated by recurrently optimizing the neural network with a self-supervised task, is adopted as the ground-truth for the unlabeled images to augment the training set and boost the segmentation accuracy.

Semantic Segmentation Semi-supervised Medical Image Segmentation

GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy

no code implementations20 Jul 2020 Shaoteng Liu, Lijun Gong, Kai Ma, Yefeng Zheng

In this paper, we propose a Graph REsidual rE-ranking Network (GREEN) to introduce a class dependency prior into the original image classification network.

Classification General Classification +3

Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications

no code implementations20 Jul 2020 Lijun Gong, Kai Ma, Yefeng Zheng

We formulate a novel distractor-aware loss that encourages large distance between the original image and its distractor in the feature space.

Classification Diabetic Retinopathy Grading +1

Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification

no code implementations18 Jul 2020 Wenao Ma, Shuang Yu, Kai Ma, Jiexiang Wang, Xinghao Ding, Yefeng Zheng

In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation.

Classification General Classification +1

Superpixel-Guided Label Softening for Medical Image Segmentation

no code implementations17 Jul 2020 Hang Li, Dong Wei, Shilei Cao, Kai Ma, Liansheng Wang, Yefeng Zheng

If a superpixel intersects with the annotation boundary, we consider a high probability of uncertain labeling within this area.

Medical Image Segmentation Semantic Segmentation

Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations

1 code implementation15 Jul 2020 Hong-Yu Zhou, Shuang Yu, Cheng Bian, Yifan Hu, Kai Ma, Yefeng Zheng

In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way.

Learning and Exploiting Interclass Visual Correlations for Medical Image Classification

no code implementations13 Jul 2020 Dong Wei, Shilei Cao, Kai Ma, Yefeng Zheng

In this paper, we present the Class-Correlation Learning Network (CCL-Net) to learn interclass visual correlations from given training data, and produce soft labels to help with classification tasks.

General Classification Image Classification +1

Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift

no code implementations19 Jun 2020 Qinming Zhang, Luyan Liu, Kai Ma, Cheng Zhuo, Yefeng Zheng

However, \textit{domain shift} and \textit{corrupted annotations}, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice.

Denoising Medical Image Segmentation +1

Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis

1 code implementation2 May 2020 Bingyu Xin, Yifan Hu, Yefeng Zheng, Hongen Liao

We use the synthesized modalities by TC-MGAN to boost the tumor segmentation accuracy, and the results demonstrate its effectiveness.

Image Generation Tumor Segmentation

Generative Adversarial Networks for Video-to-Video Domain Adaptation

no code implementations17 Apr 2020 Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng

Two colonoscopic datasets from different centres, i. e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the performance of domain adaptation of our VideoGAN.

Domain Adaptation Translation

Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation

no code implementations3 Apr 2020 Qian Yu, Yinghuan Shi, Yefeng Zheng, Yang Gao, Jianbing Zhu, Yakang Dai

Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients.

Quality Control of Neuron Reconstruction Based on Deep Learning

no code implementations19 Mar 2020 Donghuan Lu, Sujun Zhao, Peng Xie, Kai Ma, Li-Juan Liu, Yefeng Zheng

To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper.

Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks

no code implementations23 Oct 2019 Chenglang Yuan, Cheng Bian, Hongjian Kang, Shu Liang, Kai Ma, Yefeng Zheng

In this paper, we propose an efficient and accurate end-to-end architecture for angle-closure classification and scleral spur localization.

General Classification

Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube

no code implementations5 Oct 2019 Xinrui Zhuang, Yuexiang Li, Yifan Hu, Kai Ma, Yujiu Yang, Yefeng Zheng

Witnessed the development of deep learning, increasing number of studies try to build computer aided diagnosis systems for 3D volumetric medical data.

Brain Tumor Segmentation Self-Supervised Learning +1

Uncertainty-Guided Domain Alignment for Layer Segmentation in OCT Images

no code implementations22 Aug 2019 Jiexiang Wang, Cheng Bian, Meng Li, Xin Yang, Kai Ma, Wenao Ma, Jin Yuan, Xinghao Ding, Yefeng Zheng

Automatic and accurate segmentation for retinal and choroidal layers of Optical Coherence Tomography (OCT) is crucial for detection of various ocular diseases.

Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster

1 code implementation9 Jul 2019 Qingbin Shao, Lijun Gong, Kai Ma, Hualuo Liu, Yefeng Zheng

Accurate lesion detection in computer tomography (CT) slices benefits pathologic organ analysis in the medical diagnosis process.

Lesion Detection Medical Diagnosis +1

A GLCM Embedded CNN Strategy for Computer-aided Diagnosis in Intracerebral Hemorrhage

no code implementations5 Jun 2019 Yifan Hu, Yefeng Zheng

Computer-aided diagnosis (CADx) systems have been shown to assist radiologists by providing classifications of all kinds of medical images like Computed tomography (CT) and Magnetic resonance (MR).

Computed Tomography (CT) General Classification

OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images

no code implementations5 Jun 2019 Yu Chen, Jia-Wei Chen, Dong Wei, Yuexiang Li, Yefeng Zheng

Two approaches are widely used in the literature to fuse multiple modalities in the segmentation networks: early-fusion (which stacks multiple modalities as different input channels) and late-fusion (which fuses the segmentation results from different modalities at the very end).

X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks

1 code implementation CVPR 2019 Xingde Ying, Heng Guo, Kai Ma, Jian Wu, Zheng-Xin Weng, Yefeng Zheng

Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too.

Computed Tomography (CT)

TAN: Temporal Affine Network for Real-Time Left Ventricle Anatomical Structure Analysis Based on 2D Ultrasound Videos

no code implementations1 Apr 2019 Sihong Chen, Kai Ma, Yefeng Zheng

Instead of using three networks with one dedicating to each task, we use a multi-task network to perform three tasks simultaneously.

Optical Flow Estimation

When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets

no code implementations13 Dec 2018 Hong-Yu Zhou, Avital Oliver, Jianxin Wu, Yefeng Zheng

While practitioners have had an intuitive understanding of these observations, we do a comprehensive emperical analysis and demonstrate that: (1) the gains from SSL techniques over a fully-supervised baseline are smaller when trained from a pre-trained model than when trained from random initialization, (2) when the domain of the source data used to train the pre-trained model differs significantly from the domain of the target task, the gains from SSL are significantly higher and (3) some SSL methods are able to advance fully-supervised baselines (like Pseudo-Label).

Transfer Learning

Face Completion with Semantic Knowledge and Collaborative Adversarial Learning

no code implementations8 Dec 2018 Haofu Liao, Gareth Funka-Lea, Yefeng Zheng, Jiebo Luo, S. Kevin Zhou

Unlike a conventional background inpainting approach that infers a missing area from image patches similar to the background, face completion requires semantic knowledge about the target object for realistic outputs.

Facial Inpainting Semantic Segmentation

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Select, Attend, and Transfer: Light, Learnable Skip Connections

no code implementations14 Apr 2018 Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma, S. Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Puneet Sharma, Sasa Grbic, Zhoubing Xu, Dorin Comaniciu, Ghassan Hamarneh

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.

Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network

no code implementations CVPR 2018 Zizhao Zhang, Lin Yang, Yefeng Zheng

In this work, we propose a generic cross-modality synthesis approach with the following targets: 1) synthesizing realistic looking 3D images using unpaired training data, 2) ensuring consistent anatomical structures, which could be changed by geometric distortion in cross-modality synthesis and 3) improving volume segmentation by using synthetic data for modalities with limited training samples.

Computed Tomography (CT) Image Generation +1

A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes

no code implementations19 Mar 2017 Assaf Hoogi, John W. Lambert, Yefeng Zheng, Dorin Comaniciu, Daniel L. Rubin

We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes.

Computed Tomography (CT) Lesion Detection +2

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

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