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.
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.
1 code implementation • 16 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.
1 code implementation • 16 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.
1 code implementation • 29 Apr 2022 • Wenge Liu, Yi Cheng, Hao Wang, Jianheng Tang, Yafei Liu, Ruihui Zhao, Wenjie Li, Yefeng Zheng, Xiaodan Liang
In this paper, we explore how to bring interpretability to data-driven DSMD.
no code implementations • 23 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.
no code implementations • 19 Mar 2022 • Cheng Bian, Chenglang Yuan, Kai Ma, Shuang Yu, Dong Wei, Yefeng Zheng
Second, we propose a relation prototype awareness module to make the zero-shot model aware of information contained in the prototypes.
no code implementations • 12 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.
no code implementations • 10 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.
no code implementations • 7 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.
1 code implementation • 4 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.
no code implementations • 16 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.
no code implementations • 14 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.
no code implementations • 29 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.
no code implementations • 22 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.
no code implementations • 31 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.
1 code implementation • 23 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.
no code implementations • 18 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).
no code implementations • 17 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.
1 code implementation • 17 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
1 code implementation • 12 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.
1 code implementation • 9 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.
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.
1 code implementation • 28 Sep 2021 • Zhe Xu, Yixin Wang, Donghuan Lu, Lequan Yu, Jiangpeng Yan, Jie Luo, Kai Ma, Yefeng Zheng, Raymond Kai-yu Tong
Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?
Brain Tumor Segmentation
Semi-supervised Medical Image Segmentation
+1
1 code implementation • 24 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.
1 code implementation • 11 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.
1 code implementation • Findings (EMNLP) 2021 • Zijing Ou, Qinliang Su, Jianxing Yu, Ruihui Zhao, Yefeng Zheng, Bang Liu
As a first try, we modify existing generative hashing models to accommodate the BERT embeddings.
no code implementations • 18 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.
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.
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.
no code implementations • 19 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.
no code implementations • 6 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.
no code implementations • 30 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.
no code implementations • 27 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.
1 code implementation • CVPR 2021 • Wei Ji, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Qi Bi, Jingjing Li, Hanruo Liu, Li Cheng, Yefeng Zheng
To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.
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).
1 code implementation • ACL 2021 • Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Ming Xu, Yefeng Zheng
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction.
no code implementations • 17 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.
1 code implementation • 16 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.
no code implementations • 12 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.
no code implementations • 10 Jun 2021 • Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, AnnetteKopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Bram van Ginneken, Michel Bilello, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H. Heckers, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Jennifer S. Goli Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene Vorontsov, Henkjan Huisman, James A. Meakin, Sebastien Ourselin, Manuel Wiesenfarth, Pablo Arbelaez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Namkug Kim, Ildoo Kim, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu, Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
no code implementations • 3 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.
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.
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.
1 code implementation • Findings (ACL) 2021 • Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Zhenxi Lin, Yefeng Zheng
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities.
1 code implementation • 12 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.
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.
no code implementations • 30 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.
1 code implementation • 9 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.
1 code implementation • 27 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.
1 code implementation • 26 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.
1 code implementation • 27 Jan 2021 • Suyuchen Wang, Ruihui Zhao, Xi Chen, Yefeng Zheng, Bang Liu
Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence.
no code implementations • 18 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.
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.
no code implementations • 29 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.
1 code implementation • 17 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.
no code implementations • 8 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.
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.
no code implementations • 17 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.
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.
no code implementations • 6 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).
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.
no code implementations • 29 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.
no code implementations • 29 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.
no code implementations • 24 Jul 2020 • Luyan Liu, Kai Ma, Yefeng Zheng
Edge detection is a fundamental problem in different computer vision tasks.
no code implementations • 22 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.
1 code implementation • 22 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.
no code implementations • 22 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.
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.
no code implementations • 20 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.
no code implementations • 20 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
no code implementations • 20 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.
no code implementations • 20 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.
no code implementations • 18 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.
no code implementations • 17 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.
no code implementations • 17 Jul 2020 • Xing Tao, Yuexiang Li, Wenhui Zhou, Kai Ma, Yefeng Zheng
In this paper, we propose a novel self-supervised learning framework for volumetric medical images.
1 code implementation • 15 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.
no code implementations • 13 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.
no code implementations • 19 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.
1 code implementation • 2 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.
no code implementations • 26 Apr 2020 • Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Elodie Puybareau, Younes Khoudli, Thierry Geraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu, Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen de Vente, Mitko Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt, Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nunez-Garcia, Oscar Camara, Nicolo Savioli, Pablo Lamata, Jichao Zhao
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
no code implementations • 17 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.
no code implementations • 3 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.
no code implementations • 19 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.
no code implementations • CVPR 2020 • Shuxin Wang, Shilei Cao, Dong Wei, Renzhen Wang, Kai Ma, Liansheng Wang, Deyu Meng, Yefeng Zheng
We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images.
no code implementations • 23 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.
no code implementations • 5 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.
no code implementations • 22 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.
no code implementations • 16 Jul 2019 • Sihong Chen, Weiping Yu, Kai Ma, Xinlong Sun, Xiaona Lin, Desheng Sun, Yefeng Zheng
Breast lesion detection in ultrasound video is critical for computer-aided diagnosis.
1 code implementation • 9 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.
no code implementations • 5 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).
no code implementations • 5 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).
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.
no code implementations • 1 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.
4 code implementations • 1 Apr 2019 • Sihong Chen, Kai Ma, Yefeng Zheng
The performance on deep learning is significantly affected by volume of training data.
no code implementations • 14 Dec 2018 • Cheng Bian, Xin Yang, Jianqiang Ma, Shen Zheng, Yu-An Liu, Reza Nezafat, Pheng-Ann Heng, Yefeng Zheng
Accurately segmenting left atrium in MR volume can benefit the ablation procedure of atrial fibrillation.
no code implementations • 13 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).
no code implementations • 8 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.
1 code implementation • 5 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.
1 code implementation • 8 May 2018 • Saeid Asgari Taghanaki, Yefeng Zheng, S. Kevin Zhou, Bogdan Georgescu, Puneet Sharma, Daguang Xu, Dorin Comaniciu, Ghassan Hamarneh
The output imbalance refers to the imbalance between the false positives and false negatives of the inference model.
no code implementations • 14 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.
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.
no code implementations • 19 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.
no code implementations • 7 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.