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arXiv 2024年

作者： Peng, Yuanyuan Lin, Aidi Wang, Meng Lin, Tian Zou, Ke Cheng, Yinglin Shi, Tingkun Liao, Xulong Feng, Lixia Liang, Zhen Chen, Xinjian Fu, Huazhu Chen, Haoyu School of Biomedical Engineering Anhui Medical University Anhui Hefei230032 China Joint Shantou International Eye Center Shantou University The Chinese University of Hong Kong Guangdong Shantou515041 China Beth Israel Deaconess Medical Center Harvard Medical School 330 Brookline Ave BostonMA02215 United States National Key Laboratory of Fundamental Science on Synthetic Vision The College of Computer Science Sichuan University Sichuan Chengdu610065 China Department of Ophthalmology First Affiliated Hospital of Anhui Medical University Anhui Hefei China School of Electronics and Information Engineering Soochow University Jiangsu Suzhou215006 China State Key Laboratory of Radiation Medicine and Protection Soochow University Suzhou Suzhou215006 China 1 Fusionopolis Way #16-16 Connexis Singapore138632 Singapore

Inability to express the confidence level and detect unseen classes has limited the clinical implementation of artificial intelligence in the real-world. We developed a foundation model with uncertainty estimation (FMUE) to detect 11 retinal conditions on optical coherence tomography (OCT). In the internal test set, FMUE achieved a higher F1 score of 96.76% than two state-of-the-art algorithms, RETFound and UIOS, and got further improvement with thresholding strategy to 98.44%. In the external test sets obtained from other OCT devices, FMUE achieved an accuracy of 88.75% and 92.73% before and after thresholding. Our model is superior to two ophthalmologists with a higher F1 score (95.17% vs. 61.93% &71.72%). Besides, our model correctly predicts high uncertainty scores for samples with ambiguous features, of non-target-category diseases, or with low-quality to prompt manual checks and prevent misdiagnosis. FMUE provides a trustworthy method for automatic retinal anomalies detection in the real-world clinical open set environment. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical tomography

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EDEN: Deep feature distribution pooling for Saimaa ringed seals pattern matching

arXiv

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arXiv 2021年

作者： Chelak, Ilia Nepovinnykh, Ekaterina Eerola, Tuomas Kälviäinen, Heikki Belykh, Igor Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Lappeenranta-Lahti University of Technology LUT School of Engineering Science Department of Computational Engineering Computer Vision and Pattern Recognition Laboratory P.O.Box 20 Lappeenranta53850 Finland

In this paper, pelage pattern matching is considered to solve the individual re-identification of the Saimaa ringed seals. Animal re-identification together with the access to large amount of image material through camera traps and crowd-sourcing provide novel possibilities for animal monitoring and conservation. We propose a novel feature pooling approach that allow aggregating the local pattern features to get a fixed size embedding vector that incorporate global features by taking into account the spatial distribution of features. This is obtained by eigen decomposition of covariances computed for probability mass functions representing feature maps. Embedding vectors can then be used to find the best match in the database of known individuals allowing animal re-identification. The results show that the proposed pooling method outperforms the existing methods on the challenging Saimaa ringed seal image data. © 2021, CC BY-NC-ND.

关键词： Pattern matching

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Shape-Aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains 23rd

Shape-Aware Meta-learning for Generalizing Prostate MRI Segm...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Liu, Quande Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong T Stone Robotics Institute The Chinese University of Hong Kong Shatin Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030597122

Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and meta-test during training. Importantly, considering the deficiencies encountered when applying a segmentation model to unseen domains (i.e., incomplete shape and ambiguous boundary of the prediction masks), we further introduce two complementary loss objectives to enhance the meta-optimization, by particularly encouraging the shape compactness and shape smoothness of the segmentations under simulated domain shift. We evaluate our method on prostate MRI data from six different institutions with distribution shifts acquired from public datasets. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains (Code and dataset are available at https://***/liuquande/SAML). © 2020, Springer Nature Switzerland AG.

关键词： Learning systems

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δ-Calculus:A New Approach to Quantifying Location Privacy

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computers, Materials & Continua 2020年第6期63卷 1323-1342页

作者： Lihua Yin Ran Li Jingquan Ding Xiao Li Yunchuan Guo Huibing Zhang Ang Li Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Institute of Information Engineering CASBeijing100093China Xinjiang Technical Institute of Physics&Chemistry Chinese Academy ScienceUrumqi830011China Key laboratory of Speech Language Information Processing of Xinjiang Urumqi830046China University of Chinese Academy of Sciences Beijing100049China Guilin University of Electronic Technology Guilin541004China Department of Electrical and Computer Engineering Duke UniversityDurham27708USA

With the rapid development of mobile wireless Internet and high-precision localization devices,location-based services(LBS)bring more convenience for people over recent *** LBS,if the original location data are directly provided,serious privacy problems *** a response to these problems,a large number of location-privacy protection mechanisms(LPPMs)(including formal LPPMs,FLPPMs,etc.)and their evaluation metrics have been proposed to prevent personal location information from being leakage and quantify privacy ***,existing schemes independently consider FLPPMs and evaluation metrics,without synergizing them into a unifying *** this paper,a unified model is proposed to synergize FLPPMs and evaluation *** detail,the probabilistic process calculus(calledδ-calculus)is proposed to characterize obfuscation schemes(which is a LPPM)and integrateα-entropy toδ-calculus to evaluate its privacy ***,we use two calculus moving and probabilistic choice to model nodes’mobility and compute its probability distribution of nodes’locations,and a renaming function to model privacy *** formally defining the attacker’s ability and extending relative entropy,an evaluation algorithm is proposed to quantify the leakage of location ***,a series of examples are designed to demonstrate the efficiency of our proposed approach.

关键词： Location privacy δ-calculus relative entropy

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QoS-oriented Joint Resource and Trajectory Optimization in NOMA-Enhanced UAV-MEC Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Zhou, Huan Lu, Yadong Min, Geyong Yu, Zhiwen Wang, Liang Zhang, Yao Guo, Bin Northwestern Polytechnical University School of Computer Science Xi'an710129 China China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom Harbin Engineering University Ha'erbin 150001 China

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has received extensive attention because it provides resilient computation services for multiple Mobile Users (MUs). However, due to the increasing scale of offloaded tasks, the uncertain mobility of MUs, and the limited energy budget of UAV and MUs, it is extremely challenging to achieve satisfactory Quality-of-Service (QoS). Non-Orthogonal Multiple Access (NOMA), a promising technology to serve multiple MUs with limited communication resources, has great potential to be integrated with MEC. To this end, this paper proposes a QoSoriented NOMA-enhanced UAV-MEC system, which aims to capture the potential gains of uplink NOMA and enable more MUs to benefit from edge computing servers in resource-constrained UAV-assisted MEC environments. This synergy reduces MUs' uplink energy consumption but poses new challenges in resource allocation and UAV trajectory design. To address these challenges, we define a new metric called System Overhead Ratio (SOR) to reflect the system's QoS, and then consider a joint optimization problem of resource allocation, transmission power control, and UAV trajectory design, with the goal of minimizing the SOR. Given the NP-hard nature of the optimization problem, we propose a Lyapunov and convex optimization-based Low-complexity Online Resource allocation and Trajectory optimization method (LORT) to solve it, and further analyze the convergence and complexity of LORT. Finally, extensive simulations show that the proposed method surpasses other benchmarks, reducing the SOR by approximately 10%-25% under various scenarios. © 2002-2012 IEEE.

关键词： Micro air vehicle (MAV)

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Dual-Teacher++: Exploiting intra-domain and Inter-domain knowledge with reliable transfer for cardiac segmentation

arXiv

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arXiv 2021年

作者： Li, Kang Wang, Shujun Yu, Lequan Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Department of Radiation Oncology Stanford University Palo AltoCA94306 United States

Annotation scarcity is a long-standing problem in medical image analysis area. To efficiently leverage limited annotations, abundant unlabeled data are additionally exploited in semi-supervised learning, while well-established cross-modality data are investigated in domain adaptation. In this paper, we aim to explore the feasibility of concurrently leveraging both unlabeled data and cross-modality data for annotation-efficient cardiac segmentation. To this end, we propose a cutting-edge semisupervised domain adaptation framework, namely Dual-Teacher++. Besides directly learning from limited labeled target domain data (e.g., CT) via a student model adopted by previous literature, we design novel dual teacher models, including an inter-domain teacher model to explore cross-modality priors from source domain (e.g., MR) and an intra-domain teacher model to investigate the knowledge beneath unlabeled target domain. In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation. Moreover, to encourage reliable dual-domain knowledge transfer, we enhance the inter-domain knowledge transfer on the samples with higher similarity to target domain after appearance alignment, and also strengthen intra-domain knowledge transfer of unlabeled target data with higher prediction confidence. In this way, the student model can obtain reliable dual-domain knowledge and yield improved performance on target domain data. We extensively evaluated the feasibility of our method on the MM-WHS 2017 challenge dataset. The experiments have demonstrated the superiority of our framework over other semi-supervised learning and domain adaptation methods. Moreover, our performance gains could be yielded in bidirections, i.e., adapting from MR to CT, and from CT to MR. Our code will be available at https://***/kli-lalala/Dual-Teacher-. © 2021, CC0.

关键词： Domain Knowledge

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TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

arXiv

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arXiv 2023年

作者： Chen, Yuqian Zekelman, Leo R. Zhang, Chaoyi Xue, Tengfei Song, Yang Makris, Nikos Rathi, Yogesh Golby, Alexandra J. Cai, Weidong Zhang, Fan O’Donnell, Lauren J. Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States School of Computer Science The University of Sydney SydneyNSW Australia Department of Neurosurgery Brigham and Women's Hospital Harvard Medical School BostonMA United States Speech and Hearing Bioscience and Technology Harvard Medical School BostonMA United States School of Computer Science and Engineering University of New South Wales SydneyNSW Australia Center for Morphometric Analysis Departments of Psychiatry and Neurology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School BostonMA United States Psychiatry Neuroimaging Laboratory Department of Psychiatry Brigham and Women's Hospital Harvard Medical School BostonMA United States University of Electronic Science and Technology of China Sichuan Chengdu China

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemisphere

关键词： Brain

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Effective Wavenet Adaptation for Voice Conversion with Limited Data

Effective Wavenet Adaptation for Voice Conversion with Limit...

引用

IEEE International Conference on Acoustics, speech and Signal Processing

作者： Hongqiang Du Xiaohai Tian Lei Xie Haizhou Li Audio Speech and Langauge Processing Laboratory School of Computer Science Northwestern Polytechnical University China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

WaveNet has shown its great potential as a direct conversion model in voice conversion. However, due to the model complexity, WaveNet always requires a large amount of training data, which has limited its applications in voice conversion, where training data is scarce. In this paper, we propose a WaveNet adaptation method that effectively reduces the need of adaptation data. We first train a speaker independent WaveNet conversion model with multi-speaker dataset. Adaptation is then applied with limited target speaker's data. Specifically, singular value decomposition (SVD) is applied to dilated convolution layers of WaveNet to reduce the number of parameters, which makes adaptation more effective with limited data. Experiments conducted on CMU-ARCTIC and CSTR-VCTK corpus show that the proposed method outperforms baseline methods in terms of both quality and similarity.

关键词：

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One to multiple mapping dual learning: Learning multiple sources from one mixed signal

arXiv

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arXiv 2021年

作者： Liu, Ting Wang, Wenwu Zhang, Xiaofei Yan, Jingwen Guo, Yina The Department of Electronics and Communication Engineering Taiyuan University of Science and Technology Taiyuan030024 China The Centre for Vision Speech and Signal Processing University of Surrey Guildford SurreyGU2 7XH United Kingdom The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms. © 2021, CC BY.

关键词： Mapping

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MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

arXiv

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arXiv 2022年

作者： Li, Lei Wu, Fuping Wang, Sihan Luo, Xinzhe Martín-Isla, Carlos Zhai, Shuwei Zhang, Jianpeng Liu, Yanfei Zhang, Zhen Ankenbrand, Markus J. Jiang, Haochuan Zhang, Xiaoran Wang, Linhong Arega, Tewodros Weldebirhan Altunok, Elif Zhao, Zhou Li, Feiyan Ma, Jun Yang, Xiaoping Puybareau, Elodie Oksuz, Ilkay Bricq, Stephanie Li, Weisheng Punithakumar, Kumaradevan Tsaftaris, Sotirios A. Schreiber, Laura M. Yang, Mingjing Liu, Guocai Xia, Yong Wang, Guotai Escalera, Sergio Zhuang, Xiahai School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Departament de Matemàtiques & Informàtica Universitat de Barcelona Barcelona Spain Computer Vision Center Universitat Autònoma de Barcelona Spain School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Northwestern Polytechnical University Xi'An China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China College of Physics and Information Engineering Fuzhou University Fuzhou China Comprehensive Heart Failure Center Wuerzburg University Hospitals Wuerzburg Germany School of Engineering University of Edinburgh Edinburgh United Kingdom School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Canada Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecomm-Unications Chongqing China ImViA Laboratory Université Bourgogne Franche-Comté Dijon France Computer Engineering Department Istanbul Technical University Istanbul Turkey Le Kremlin-Bicêtre France Department of Mathematics Nanjing University of Science and Technology Nanjing China Department of Mathematics Nanjing University Nanjing China

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (***/zhuangxiahai/0/myops20/). © 2022, CC BY-NC-SA.

关键词： Magnetic resonance imaging

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