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Specificity-Preserving Federated Learning for MR Image Reconstruction

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Wang, Shanshan Xu, Yong Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology CAS Shenzhen518055 China Terminus Group China

Federated learning (FL) can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction by enabling multiple institutions to collaborate without needing to aggregate local data. However, the domain shift caused by different MR imaging protocols can substantially degrade the performance of FL models. Recent FL techniques tend to solve this by enhancing the generalization of the global model, but they ignore the domain-specific features, which may contain important information about the device properties and be useful for local reconstruction. In this paper, we propose a specificity-preserving FL algorithm for MR image reconstruction (FedMRI). The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution. Such scheme is then executed in the frequency space and the image space respectively, allowing exploration of generalized representation and client-specific properties simultaneously in different spaces. Moreover, to further boost the convergence of the globally shared encoder when a domain shift is present, a weighted contrastive regularization is introduced to directly correct any deviation between the client and server during optimization. Extensive experiments demonstrate that our FedMRI’s reconstructed results are the closest to the ground-truth for multi-institutional data, and that it outperforms state-of-the-art FL methods. © 2021, CC BY.

关键词： Image reconstruction

Evaluating the Covid-19 Screening Regime for Cross-Border Workers

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Evaluating the Covid-19 Screening Regime for Cross-Border Wo...

Simulation Winter Conference

作者： Yiqi Seow Xiao Feng Yin Haiyan Xu Xiuju Fu Zheng Qin Hong Kiat Tan Li Yang Hsu Kiesha Prem Sze Wee Tan Molecular Engineering Laboratory Institute of Bioengineering and Nanotechnology A*STAR Singapore Systems Science Department Institute of High Performance Computing A*STAR Singapore Industry Development Group A*STAR Singapore Saw Swee Hock School of Public Health National University of Singapore and National University Health System Singapore Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London United Kingdom Enterprise Division A*STAR Singapore

ISBN: (纸本)9781665476621

关键词： COVID-19 Schedules Pandemics Sociology Social factors Frequency measurement Statistics

Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

A Cognitive Analytics based Approach for Machine Health Monitoring, Anomaly Detection, and Predictive Maintenance

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A Cognitive Analytics based Approach for Machine Health Moni...

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Farzam Farbiz Yuan Miaolong Zhou Yu A*STAR Institute of High Performance Computing (IHPC) Singapore Smart Manufacturing Group A*STAR Advanced Remanufacturing and Technology Centre (ARTC) Singapore Project Management Office A*STAR Advanced Remanufacturing and Technology Centre (ARTC) Singapore

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

Traditionally, there are two major limitations for machine learning (ML)-assisted manufacturing applications. First, it would require a tremendous amount of manual data annotations for ML models. Second, ML models are often learned offline and unable to capture the machine dynamism and adapt to changes over the time. In this paper, we propose a framework based on the concept of cognitive analytics with unsupervised learning for machine health monitoring, anomaly detection and predictive maintenance. The experimental results on an industrial robot demonstrates the effectiveness of the proposed framework in the identified use case.

关键词： Adaptation models Service robots Manufacturing Monitoring Anomaly detection Unsupervised learning Predictive maintenance

Perovskite-type YRh3B with multiple types of nodal point and nodal line states

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Physical Review B 2021年第24期103卷 245126-245126页

作者： Feng Zhou Chaoxi Cui Jianhua Wang Minquan Kuang Tie Yang Zhi-Ming Yu Xiaotian Wang Gang Zhang Zhenxiang Cheng School of Physical Science and Technology Southwest University Chongqing 400715 China Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems and School of Physics Beijing Institute of Technology Beijing 100081 China Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) 138632 Singapore Institute for Superconducting and Electronic Materials (ISEM) University of Wollongong Wollongong 2500 Australia

Experimentally synthesized perovskite-type YRh3B with a Pm3¯m type structure was proposed as a topological material (TM) via first-principles calculations and the low-energy k·p effective Hamiltonian, which has a quadratic contact triple point (QCTP) at point Γ and six pairs of open nodal lines (NLs) of the hybrid type. Clear surface states observed in the surface spectrum confirmed the topological states. When spin-orbit coupling was considered, the QCTP at Γ transferred to the quadratic-type Dirac nodal point (NP). Under 1% tetragonal strained lattice constants, YRh3B hosted richer topological states, including a quadratic-type twofold degenerate NP, six pairs of open NLs of the hybrid type, and two closed NLs of type I and hybrid type. Moreover, it was proved that the NLs of YRh3B at its strained lattice constants contain all types of band-crossing points (BCPs) (i.e., type I, type II, and critical type). Such rich types of NP and NL states in one compound make it potentially applicable for multifunctional electronic devices as well as an appropriate platform to study entanglement among topological states.

关键词： Density functional theory First-principles calculations Tight-binding model k dot p method

Interventional video grounding with dual contrastive learning

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arXiv 2021年

作者： Nan, Guoshun Qiao, Rui Xiao, Yao Liu, Jun Leng, Sicong Zhang, Hao Lu, Wei StatNLP Research Group Singapore University of Technology and Design Singapore Singapore Shanghai Jiao Tong University China Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of High Performance Computing A*STAR Singapore Singapore

Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with various likelihood-based matching or regression strategies, i.e., P(Y |X). Consequently, these models may suffer from spurious correlations between the language and video features due to the selection bias of the dataset. 1) To uncover the causality behind the model and data, we first propose a novel paradigm from the perspective of the causal inference, i.e., interventional video grounding (IVG) that leverages backdoor adjustment to deconfound the selection bias based on structured causal model (SCM) and do-calculus P(Y |do(X)). Then, we present a simple yet effective method to approximate the unobserved confounder as it cannot be directly sampled from the dataset. 2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations. Experiments on three standard benchmarks show the effectiveness of our approaches. Our code is available on GitHub: https://***/nanguoshun/IVG. © 2021, CC BY.

关键词： Calculations

Symmetry-enforced ideal lanternlike phonons in the ternary nitride Li6WN4

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Physical Review B 2021年第4期104卷 L041104-L041104页

作者： Xiaotian Wang Feng Zhou Tie Yang Minquan Kuang Zhi-Ming Yu Gang Zhang School of Physical Science and Technology Southwest University Chongqing 400715 China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing 100081 China Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore 138632 Singapore

Topological physics of phonon spectra has been attracting great interest. Using the first-principle calculations and symmetry analysis, we show the realistic ternary nitride Li6WN4 features ideal (nearly flat) nodal-surface and nodal-line structures in its phonon spectra. These nodal degeneracies are shaped like lanterns, and their existence is guaranteed by nonsymmorphic symmetry. The corresponding topological phonon surface state covers exactly half the surface Brillouin zone and can thereby be distinguished from those of conventional nodal-line and nodal-surface semimetals. Our study enriches the classification of topological quantum phases, predicts ideal material candidates, and provides a good platform for investigating the interaction between nodal-line and nodal-surface phonons.

关键词： First-principles calculations Phonons Topological materials

Observation of Electric Hysteresis, Polarization Oscillation, and Pyroelectricity in Nonferroelectric p−n Heterojunctions

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Physical Review Letters 2023年第19期130卷 196801-196801页

作者： Yucheng Jiang Xinglong Ma Lin Wang Jinlei Zhang Zhichao Wang Run Zhao Guozhen Liu Yang Li Cheng Zhang Chunlan Ma Yaping Qi Lin Wu Ju Gao Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou 215009 China School of Physics Nanjing University Nanjing 210093 China School of Materials Science and Engineering Shanghai University Shanghai 200444 China Macau Institute of Systems Engineering Macau University of Science and Technology Macau 999078 China Advanced Institute for Materials Research Tohoku University Sendai 980-8577 Japan Science Mathematics and Technology Singapore University of Technology and Design 8 Somapah Road Singapore 487372 Singapore Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore School for Optoelectronic Engineering Zaozhuang University Shandong 277160 China

The switchable electric polarization is usually achieved in ferroelectric materials with noncentrosymmetric structures, which opens exciting opportunities for information storage and neuromorphic computing. In another polar system of p−n junction, there exists the electric polarization at the interface due to the Fermi level misalignment. However, the resultant built-in electric field is unavailable to manipulate, thus attracting less attention for memory devices. Here, we report the interfacial polarization hysteresis (IPH) in the vertical sidewall van der Waals heterojunctions of black phosphorus and quasi-two-dimensional electron gas on SrTiO3. A nonvolatile switching of electric polarization can be achieved by reconstructing the space charge region (SCR) with long-lifetime nonequilibrium carriers. The resulting electric-field controllable IPH is experimentally verified by electric hysteresis, polarization oscillation, and pyroelectric effect. Further studies confirm the transition temperature of 340 K, beyond which the IPH vanishes. The second transition is revealed with the temperature dropping below 230 K, corresponding to the sharp improvement of IPH and the freezing of SCR reconstruction. This work offers new possibilities for exploring the memory phenomena in nonferroelectric p−n heterojunctions.

关键词： Electric polarization Electroresistance Pyroelectricity

Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

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arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology