• Complex
  • Title
  • Author
  • Keyword
  • Abstract
  • Scholars
Search
High Impact Results & Cited Count Trend for Year Keyword Cloud and Partner Relationship

Query:

学者姓名:孟德宇

Refining:

Source

Submit Unfold

Co-Author

Submit Unfold

Language

Submit

Clean All

Export Sort by:
Default
  • Default
  • Title
  • Year
  • WOS Cited Count
  • Impact factor
  • Ascending
  • Descending
< Page ,Total 17 >
LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images EI
会议论文 | 2021 , 12587 LNCS , 116-121 | Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Abstract&Keyword Cite

Abstract :

The thyroid nodule is quickly increasing worldwide and the thyroid ultrasound is the key tool for the diagnosis of it. For the subtle difference between malignant and benign nodules, segmenting lesions is the crucial preliminary step for diagnosis. In this paper, we propose a low-resolution-to-high-resolution segmentation framework for TN-SCUI2020 challenge to alleviate the workload of clinicians and improve the efficiency of diagnosis. Specifically speaking, in order to integrate multi-scale information, several low-resolution segmenting results are obtained firstly and combined with a high-resolution image to refine them and obtain high-resolution results. Secondly, iterative-transfer is proposed to effectively initialize network based on previous trained one on small-scale images. Finally, ensemble refinement is introduced to utilize multiple models to refine the segmentation again. Experimental results showed the effectiveness of the proposed framework. And we won the 2nd place in the segmentation task of TN-SCUI2020. © 2021, Springer Nature Switzerland AG.

Keyword :

Magnetic resonance imaging Medical imaging Ultrasonics Image segmentation Computerized tomography Diagnosis

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Chen, Huai , Song, Shaoli , Wang, Xiuying et al. LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images [C] . 2021 : 116-121 .
MLA Chen, Huai et al. "LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images" . (2021) : 116-121 .
APA Chen, Huai , Song, Shaoli , Wang, Xiuying , Wang, Renzhen , Meng, Deyu , Wang, Lisheng . LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images . (2021) : 116-121 .
Export to NoteExpress RIS BibTex
Scaled Simplex Representation for Subspace Clustering EI SCIE
期刊论文 | 2021 , 51 (3) , 1493-1505 | IEEE Transactions on Cybernetics
WoS CC Cited Count: 4
Abstract&Keyword Cite

Abstract :

The self-expressive property of data points, that is, each data point can be linearly represented by the other data points in the same subspace, has proven effective in leading subspace clustering (SC) methods. Most self-expressive methods usually construct a feasible affinity matrix from a coefficient matrix, obtained by solving an optimization problem. However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations. This consequently damages the inherent correlations among the data. Besides, the affine constraint used in these methods is not flexible enough for practical applications. To overcome these problems, in this article, we introduce a scaled simplex representation (SSR) for the SC problem. Specifically, the non-negative constraint is used to make the coefficient matrix physically meaningful, and the coefficient vector is constrained to be summed up to a scalar s © 2013 IEEE.

Keyword :

Matrix algebra Clustering algorithms Vectors

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Xu, Jun , Yu, Mengyang , Shao, Ling et al. Scaled Simplex Representation for Subspace Clustering [J]. | IEEE Transactions on Cybernetics , 2021 , 51 (3) : 1493-1505 .
MLA Xu, Jun et al. "Scaled Simplex Representation for Subspace Clustering" . | IEEE Transactions on Cybernetics 51 . 3 (2021) : 1493-1505 .
APA Xu, Jun , Yu, Mengyang , Shao, Ling , Zuo, Wangmeng , Meng, Deyu , Zhang, Lei et al. Scaled Simplex Representation for Subspace Clustering . | IEEE Transactions on Cybernetics , 2021 , 51 (3) , 1493-1505 .
Export to NoteExpress RIS BibTex
Hyperspectral Image Restoration Combining Intrinsic Image Characterization with Robust Noise Modeling EI SCIE
期刊论文 | 2021 , 14 , 1628-1644 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Abstract&Keyword Cite

Abstract :

In hyperspectral image (HSI) processing, a fundamental issue is to restore HSI data from various degradations such as noise corruption and information missing. However, most existing methods more or less ignore the abundant prior knowledge on HSIs and the embedded noise, leading to suboptimal performance in practice. In this article, we propose a novel HSI restoration method by fully considering the intrinsic image structures and the complex noise characteristics. For HSIs, the global correlation is captured by the Kronecker-basis-representation-based tensor low-rankness measure, which integrates the insights delivered by both CP and Tucker decompositions; the local regularity is depicted by a plug-and-play spatial-spectral convolutional neural network with strong fitting ability to complex image features. For realistic noise, its statistical characteristics are encoded by a nonidentical and nonindependent distributed mixture of Gaussians distribution with flexible fitting capability. Then, we incorporate these image and noise priors into a probabilistic model based on the maximum a posteriori principle, and develop a solving scheme by combining expectation-maximization and alternating direction method of multipliers. Extensive experimental results on both simulated and real scenarios demonstrate the effectiveness of the proposed method and its superiority over the compared state-of-the- arts. © 2008-2012 IEEE.

Keyword :

Restoration Maximum principle Image reconstruction Image segmentation Complex networks Spectroscopy Convolutional neural networks

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Ma, Tian-Hui , Xu, Zongben , Meng, Deyu et al. Hyperspectral Image Restoration Combining Intrinsic Image Characterization with Robust Noise Modeling [J]. | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2021 , 14 : 1628-1644 .
MLA Ma, Tian-Hui et al. "Hyperspectral Image Restoration Combining Intrinsic Image Characterization with Robust Noise Modeling" . | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021) : 1628-1644 .
APA Ma, Tian-Hui , Xu, Zongben , Meng, Deyu , Zhao, Xi-Le . Hyperspectral Image Restoration Combining Intrinsic Image Characterization with Robust Noise Modeling . | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2021 , 14 , 1628-1644 .
Export to NoteExpress RIS BibTex
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network CPCI-S
会议论文 | 2021 , 12908 , 132-142 | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Abstract&Keyword Cite

Abstract :

The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei grading aims to improve pathologists' work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most of the existing nuclei segmentation and classification methods can not handle the inter-class similarity property of nuclei grading, thus can not be directly applied to the ccRCC grading task. In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading. Specifically, we propose a segmentation network called W-Net that can separate the clustered nuclei. Then, we recast the fine-grained nuclei classification into two cross-category classification tasks that are leaned by two newly designed high-resolution feature extractors (HRFEs). The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited to the classification task. Last, a head-fusion block is applied to generate the predicted label of each nucleus. Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000 image patches with 70945 annotated nuclei. We demonstrate that our proposed method achieves state-of-the-art performance compared to existing methods on this large ccRCC grading dataset.

Keyword :

Nuclei grading Histopathology Nuclei segmentation

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Gao, Zeyu , Shi, Jiangbo , Zhang, Xianli et al. Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network [C] . 2021 : 132-142 .
MLA Gao, Zeyu et al. "Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network" . (2021) : 132-142 .
APA Gao, Zeyu , Shi, Jiangbo , Zhang, Xianli , Li, Yang , Zhang, Haichuan , Wu, Jialun et al. Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network . (2021) : 132-142 .
Export to NoteExpress RIS BibTex
InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction CPCI-S
会议论文 | 2021 , 12906 , 107-118 | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Abstract&Keyword Cite

Abstract :

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. Against these issues, we propose a novel interpretable dual domain network, termed as InDuDoNet, which combines the advantages of model-driven and data-driven methodologies. Specifically, we build a joint spatial and Radon domain reconstruction model and utilize the proximal gradient technique to design an iterative algorithm for solving it. The optimization algorithm only consists of simple computational operators, which facilitate us to correspondingly unfold iterative steps into network modules and thus improve the interpretablility of the framework. Extensive experiments on synthesized and clinical data show the superiority of our InDuDoNet. Code is available in littps://github.com/hongwang01/InDuDoNet.

Keyword :

Generalization ability Imaging geometry Metal artifact reduction Multi-class segmentation Physical interpretability

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Wang, Hong , Li, Yuexiang , Zhang, Haimiao et al. InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction [C] . 2021 : 107-118 .
MLA Wang, Hong et al. "InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction" . (2021) : 107-118 .
APA Wang, Hong , Li, Yuexiang , Zhang, Haimiao , Chen, Jiawei , Ma, Kai , Meng, Deyu et al. InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction . (2021) : 107-118 .
Export to NoteExpress RIS BibTex
IIT-GAT: Instance-level image transformation via unsupervised generative attention networks with disentangled representations EI SCIE
期刊论文 | 2021 , 225 | KNOWLEDGE-BASED SYSTEMS
Abstract&Keyword Cite

Abstract :

Image-to-image translation is an important research field in computer vision, which is widely associated with Generative Adversarial Networks (GANs) and dual learning. However, the existing methods mainly translate the global image of the source domain to the target domain, which fails to implement instance-level image-to-image translation, and the translation results in the target domain cannot be controlled. In this paper, an instance-level image-to-image translation network (IIT-GAT) is proposed, which includes attention module and feature-encoder module. The attention module is used to guide our model to focus on more interesting instance to generate instance masks, which helps to separate instance and background of an image. The feature-encoder module is used to embed the images into two different spaces: domain-invariant content space and domain-specific attribute space. The content features and attribute features of different images are used as input to generator simultaneously to improve the controllability of image-to-image translation. To this end, we introduce a local self-reconstruction loss that encourages the network to learn the style feature of target instances. Generally, our method not only improves the quality of instance-level image-to-image translation, but also increases controllability on this basis. Extensive experiments are conducted on multiple datasets to validate the effectiveness of the proposed framework, and the results show our method has better performance than previous methods. (C) 2021 Elsevier B.V. All rights reserved.

Keyword :

Attention mechanism Disentangled representation Generative adversarial networks Image-to-image translation

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Shao, Mingwen , Zhang, Youcai , Fan, Yuan et al. IIT-GAT: Instance-level image transformation via unsupervised generative attention networks with disentangled representations [J]. | KNOWLEDGE-BASED SYSTEMS , 2021 , 225 .
MLA Shao, Mingwen et al. "IIT-GAT: Instance-level image transformation via unsupervised generative attention networks with disentangled representations" . | KNOWLEDGE-BASED SYSTEMS 225 (2021) .
APA Shao, Mingwen , Zhang, Youcai , Fan, Yuan , Zuo, Wangmeng , Meng, Deyu . IIT-GAT: Instance-level image transformation via unsupervised generative attention networks with disentangled representations . | KNOWLEDGE-BASED SYSTEMS , 2021 , 225 .
Export to NoteExpress RIS BibTex
Selective generative adversarial network for raindrop removal from a single image EI SCIE
期刊论文 | 2021 , 426 , 265-273 | Neurocomputing
Abstract&Keyword Cite

Abstract :

The removal of raindrops from a single image is still challenging because of the diversity and density of raindrops existing in the rainy image. Moreover, the colors of raindrops are constantly changing with the background which also makes the raindrops cannot be well removed by using the current methods. In this paper, we tackle these limitations by combining the raindrops shape features with the background structure features to guide the network to accurately remove raindrops. Specifically, we propose a selective skip connection GAN (SSCGAN) combining the selective skip connection and self-attention mechanism to restoring the clean image from a raindrop degraded one. Our main idea is selectively transmitting the information of raindrops to the decoder through Gated Recurrent Units (GRU) to better generate a clean image. During the training, the selective skip connection model (SSCM) extract raindrops binary mask from the rainy image and eliminate the interference of background noise. Simultaneously, we use self-attention blocks (SABs) to make the generator network pay more attention to global structure features of the rainy image and conversely correct the raindrops binary mask. Experiments show that our method has better performance than previous methods. © 2020 Elsevier B.V.

Keyword :

Recurrent neural networks Drops

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Shao, Mingwen , Li, Le , Wang, Hong et al. Selective generative adversarial network for raindrop removal from a single image [J]. | Neurocomputing , 2021 , 426 : 265-273 .
MLA Shao, Mingwen et al. "Selective generative adversarial network for raindrop removal from a single image" . | Neurocomputing 426 (2021) : 265-273 .
APA Shao, Mingwen , Li, Le , Wang, Hong , Meng, Deyu . Selective generative adversarial network for raindrop removal from a single image . | Neurocomputing , 2021 , 426 , 265-273 .
Export to NoteExpress RIS BibTex
Uncertainty Guided Multi-Scale Attention Network for Raindrop Removal From a Single Image EI SCIE
期刊论文 | 2021 , 30 , 4828-4839 | IEEE TRANSACTIONS ON IMAGE PROCESSING
Abstract&Keyword Cite

Abstract :

Raindrops adhered to a glass window or camera lens appear in various blurring degrees and resolutions due to the difference in the degrees of raindrops aggregation. The removal of raindrops from a rainy image remains a challenging task because of the density and diversity of raindrops. The abundant location and blur level information are strong prior guide to the task of raindrop removal. However, existing methods use a binary mask to locate and estimate the raindrop with the value 1 (adhesion of raindrops) and 0 (no adhesion), which ignores the diversity of raindrops. Meanwhile, it is noticed that different scale versions of a rainy image have similar raindrop patterns, which makes it possible to employ such complementary information to represent raindrops. In this work, we first propose a soft mask with the value in [-1,1] indicating the blurring level of the raindrops on the background, and explore the positive effect of the blur degree attribute of raindrops on the task of raindrop removal. Secondly, we explore the multi-scale fusion representation for raindrops based on the deep features of the input multi-scale images. The framework is termed uncertainty guided multi-scale attention network (UMAN). Specifically, we construct a multi-scale pyramid structure and introduce an iterative mechanism to extract blur-level information about raindrops to guide the removal of raindrops at different scales. We further introduce the attention mechanism to fuse the input image with the blur-level information, which will highlight raindrop information and reduce the effects of redundant noise. Our proposed method is extensively evaluated on several benchmark datasets and obtains convincing results.

Keyword :

multi-scale network Rain Uncertainty Image edge detection Kernel Image restoration raindrops removal Uncertainty guided Task analysis attention fusion Degradation

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Shao, Ming-Wen , Li, Le , Meng, De-Yu et al. Uncertainty Guided Multi-Scale Attention Network for Raindrop Removal From a Single Image [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2021 , 30 : 4828-4839 .
MLA Shao, Ming-Wen et al. "Uncertainty Guided Multi-Scale Attention Network for Raindrop Removal From a Single Image" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 30 (2021) : 4828-4839 .
APA Shao, Ming-Wen , Li, Le , Meng, De-Yu , Zuo, Wang-Meng . Uncertainty Guided Multi-Scale Attention Network for Raindrop Removal From a Single Image . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2021 , 30 , 4828-4839 .
Export to NoteExpress RIS BibTex
Alternative Baselines for Low-Shot 3D Medical Image Segmentation-An Atlas Perspective CPCI-S
会议论文 | 2021 , 35 , 634-642 | 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence
Abstract&Keyword Cite

Abstract :

Low-shot (one/few-shot) segmentation has attracted increasing attention as it works well with limited annotation. State-of-the-art low-shot segmentation methods on natural images usually focus on implicit representation learning for each novel class, such as learning prototypes, deriving guidance features via masked average pooling, and segmenting using cosine similarity in feature space. We argue that low-shot segmentation on medical images should step further to explicitly learn dense correspondences between images to utilize the anatomical similarity. The core ideas are inspired by the classical practice of multi-atlas segmentation, where the indispensable parts of atlas-based segmentation, i.e., registration, label propagation, and label fusion are unified into a single framework in our work. Specifically, we propose two alternative baselines, i.e., the Siamese-Baseline and Individual-Difference-Aware Baseline, where the former is targeted at anatomically stable structures (such as brain tissues), and the latter possesses a strong generalization ability to organs suffering large morphological variations (such as abdominal organs). In summary, this work sets up a benchmark for low-shot 3D medical image segmentation and sheds light on further understanding of atlas-based few-shot segmentation.

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Wang, Shuxin , Cao, Shilei , Wei, Dong et al. Alternative Baselines for Low-Shot 3D Medical Image Segmentation-An Atlas Perspective [C] . 2021 : 634-642 .
MLA Wang, Shuxin et al. "Alternative Baselines for Low-Shot 3D Medical Image Segmentation-An Atlas Perspective" . (2021) : 634-642 .
APA Wang, Shuxin , Cao, Shilei , Wei, Dong , Xie, Cong , Ma, Kai , Wang, Liansheng et al. Alternative Baselines for Low-Shot 3D Medical Image Segmentation-An Atlas Perspective . (2021) : 634-642 .
Export to NoteExpress RIS BibTex
Learning to Purify Noisy Labels via Meta Soft Label Corrector CPCI-S
会议论文 | 2021 , 35 , 10388-10396 | 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence
Abstract&Keyword Cite

Abstract :

Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by identifying suspected noisy labels and then correcting them. Current approaches to correcting corrupted labels usually need manually pre-defined label correction rules, which makes it hard to apply in practice due to the large variations of such manual strategies with respect to different problems. To address this issue, we propose a meta-learning model, aiming at attaining an automatic scheme which can estimate soft labels through meta-gradient descent step under the guidance of a small amount of noise-free meta data. By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, our method can adaptively obtain rectified soft labels gradually in iteration according to current training problems. Besides, our method is model-agnostic and can be combined with any other existing classification models with ease to make it available to noisy label cases. Comprehensive experiments substantiate the superiority of our method in both synthetic and real-world problems with noisy labels compared with current state-of-the-art label correction strategies.

Cite:

Copy from the list or Export to your reference management。

GB/T 7714 Wu, Yichen , Shu, Jun , Xie, Qi et al. Learning to Purify Noisy Labels via Meta Soft Label Corrector [C] . 2021 : 10388-10396 .
MLA Wu, Yichen et al. "Learning to Purify Noisy Labels via Meta Soft Label Corrector" . (2021) : 10388-10396 .
APA Wu, Yichen , Shu, Jun , Xie, Qi , Zhao, Qian , Meng, Deyu . Learning to Purify Noisy Labels via Meta Soft Label Corrector . (2021) : 10388-10396 .
Export to NoteExpress RIS BibTex
10| 20| 50 per page
< Page ,Total 17 >

Export

Results:

Selected

to

Format:
FAQ| About| Online/Total:941/98470394
Address:XI'AN JIAOTONG UNIVERSITY LIBRARY(No.28, Xianning West Road, Xi'an, Shaanxi Post Code:710049) Contact Us:029-82667865
Copyright:XI'AN JIAOTONG UNIVERSITY LIBRARY Technical Support:Beijing Aegean Software Co., Ltd.