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DU-GAN: Generative adversarial networks with dual-domain U-net based discriminators for low-dose CT denoising

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arXiv 2021年

作者： Huang, Zhizhong Zhang, Junping Zhang, Yi Shan, Hongming The Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200433 China The College of Computer Science Sichuan University Chengdu610065 China The Institute of Science and Technology for Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China

Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Over the past few years, various deep learning techniques, especially generative adversarial networks (GANs), have been introduced to improve the image quality of LDCT images through denoising, achieving impressive results over traditional approaches. GANs-based denoising methods usually leverage an additional classification network, i.e. discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly;it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT images. Furthermore, the CutMix technique enables the per-pixel outputs of the U-Net based discriminator to provide radiologists with a confidence map to visualize the uncertainty of the denoised results, facilitating the LDCT-based screening and diagnosis. Extensive experiments on the simulated and real-world datasets demonstrate superior p

关键词： Generative adversarial networks

technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collab.rative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

Click-iG: Simultaneous Enrichment and Profiling of Intact N-linked, O-GalNAc, and O-GlcNAcylated Glycopeptides

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Angewandte Chemie 2023年第36期135卷

作者： Dr. Jialin Liu Dr. Bo Cheng Dr. Xinqi Fan Xinyue Zhou Jiankun Wang Dr. Wen Zhou Hengyu Li Dr. Wenfeng Zeng Prof. Pengyuan Yang Prof. Xing Chen College of Chemistry and Molecular Engineering Beijing National Laboratory for Molecular Sciences Peking-Tsinghua Center for Life Sciences Synthetic and Functional Biomolecules Center and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education Peking University Beijing 100871 China Institute of Biomedical Sciences and Department of Chemistry Fudan University Shanghai 200433 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) and Institute of Computing Technology CAS Beijing 100190 China Deseased

Proteins are ubiquitously modified with glycans of varied chemical structures through distinct glycosidic linkages, making the landscape of protein glycosylation challenging to map. Profiling of intact glycopeptides with mass spectrometry (MS) has recently emerged as a powerful tool for revealing matched information of the glycosylation sites and attached glycans (i.e., intact glycosites), but is largely limited to individual glycosylation types. Herein, we describe Click-iG, which integrates metabolic lab.ling of glycans with clickable unnatural sugars, an optimized MS method, and a tailored version of pGlyco3 software to enable simultaneous enrichment and profiling of three types of intact glycopeptides: N-linked, mucin-type O-linked, and O-GlcNAcylated glycopeptides. We demonstrate the utility of Click-iG by the identification of thousands of intact glycosites in cell lines and living mice. From the mouse lung, heart, and spleen, a total of 2053 intact N-glycosites, 262 intact O-GalNAc glycosites, and 1947 O-GlcNAcylation sites were identified. Click-iG-enabled comprehensive coverage of the protein glycosylation landscape lays the foundation for interrogating crosstalk between different glycosylation pathways.

关键词： Glycoproteomics Glycosylation Intact Glycosites Metabolic lab.ling Unnatural Glycans

DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge

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arXiv 2020年

作者： Luo, Chunlong Yu, Tianqi Luo, Yufan Wang, Manqing Yu, Fuhai Li, Yinhao Tian, Chan Qiao, Jie Xiao, Li Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Peking University Third Hospital Beijing China School of Computer and Control Engineering University of Chinese Academy of China

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of prior knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criterion as a prior knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. 3390 metaphase images from clinical lab.ratory are collected and lab.lled to evaluate the performance. Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Chromosomes

AutoHR: A strong end-To-end baseline for remote heart rate measurement with neural searching

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arXiv 2020年

作者： Yu, Zitong Li, Xiaobai Niu, Xuesong Shi, Jingang Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Oulu90014 Finland Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing end-To-end rPPG and heart rate (HR) measurement methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and bad illumination). In this letter, we explore the reason why existing end-To-end networks perform poorly in challenging conditions and establish a strong end-Toend baseline (AutoHR) for remote HR measurement with neural architecture search (NAS). The proposed method includes three parts: 1) a powerful searched backbone with novel Temporal Difference Convolution (TDC), intending to capture intrinsic rPPGaware clues between frames;2) a hybrid loss function considering constraints from both time and frequency domains;and 3) spatiotemporal data augmentation strategies for better representation learning. Comprehensive experiments are performed on three benchmark datasets to show our superior performance on both intra-and cross-dataset testing. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

Cloud-based data-sharing scheme using verifiable and CCA-secure re-encryption from indistinguishability obfuscation 14th

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Cloud-based data-sharing scheme using verifiable and CCA-sec...

14th International Conference on information Security and Cryptology, Inscrypt 2018

作者： Zhang, Mingwu Jiang, Yan Shen, Hua Li, Bingbing Susilo, Willy School of Computers Hubei University of Technology Wuhan China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Wollongong Australia

ISBN: (纸本)9783030142339

A cloud-based re-encryption scheme allows a semi-trusted cloud proxy to convert a ciphertext under delegator’s public-key into a ciphertext of delegatee’s. However, for an untrusted cloud proxy, as the re-encryption program was outsourced on the cloud, the cloud can debug the program and might have illegal activities in practice, such as monitoring the program executing, returning an incorrect re-encryption ciphertext, or colluding with the participants to obtain the sensitive information. In this work, we propose a construction of cloud-based verifiable re-encryption by incorporating new cryptographic primitives of indistinguishability obfuscation and puncturable pseudorandom functions, which can achieve the master-secret security even if the proxy colludes with the delegatee. Furthermore, our scheme can provide the white-box security in re-encryption procedure to implement the sensitive-data protection in the presence of white-box access, and it resists on chosen-ciphertext attacks in both the first-level encryption and the second-level encryption. The decryption is very efficient since it only requires several symmetric PRF operations, which can be deployed and applied in the light-weight security device such as Mobile Phones (MPs), Wireless Body Area Networks (WBANs) and nodes in Internet-of-Things (IoTs). © 2019, Springer Nature Switzerland AG.

关键词： Data obfuscation

Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax

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Overcoming Classifier Imbalance for Long-Tail Object Detecti...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yu Li Tao Wang Bingyi Kang Sheng Tang Chunfeng Wang Jintao Li Jiashi Feng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored. In this work, we provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution. We find existing detection methods are unable to model few-shot classes when the dataset is extremely skewed, which can result in classifier imbalance in terms of parameter magnitude. Directly adapting long-tail classification models to detection frameworks can not solve this problem due to the intrinsic difference between detection and classification. In this work, we propose a novel balanced group softmax (BAGS) module for balancing the classifiers within the detection frameworks through group-wise training. It implicitly modulates the training process for the head and tail classes and ensures they are both sufficiently trained, without requiring any extra sampling for the instances from the tail classes. Extensive experiments on the very recent long-tail large vocabulary object recognition benchmark LVIS show that our proposed BAGS significantly improves the performance of detectors with various backbones and frameworks on both object detection and instance segmentation. It beats all state-of-the-art methods transferred from long-tail image classification and establishes new state-of-the-art. Code is availab.e at https://***/FishYuLi/BalancedGroupSoftmax.

关键词： Training Object detection Proposals Adaptation models Feature extraction Computational modeling Detectors

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax

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arXiv 2020年

作者： Li, Yu Wang, Tao Kang, Bingyi Tang, Sheng Wang, Chunfeng Li, Jintao Feng, Jiashi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

关键词： Object recognition

Network Parameter Generation for One-Shot Object Detection

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Network Parameter Generation for One-Shot Object Detection

2019 IEEE International Conference on Unmanned Systems, ICUS 2019

作者： Wang, Kang Zhao, Jiajia Wang, Yang Zhou, Shuigeng Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Shanghai200433 China Beijing Electro-Mechanical Engineer Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Nanchang University School of Information Engineering 330031 China

ISBN: (纸本)9781728137926

Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enough, especially for one-shot object detection where there is only one training example of each target class. In this paper, we propose a network parameter generation framework for effective one-shot object detection. By leveraging the semantic relationship between the source-domain and the target-domain (new classes), our framework can generate network parameters for the new classes directly. Furthermore, we develop two implementation schemes for this framework: weighting-based parameter generation and regression-based parameter generation. These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain. Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection. © 2019 IEEE.

关键词： Object detection