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arXiv 2020年

作者： Dou, Yongqiang Yang, Haocheng Yang, Maolin Xu, Yanyan Ke, Dengfeng School of Information Science and Technology Beijing Forestry University Beijing100083 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Engineering Research Center for Forestry-oriented Intelligent Information Processing National Forestry and Grassland Administration China

It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose D3M, to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go. Source code, analysis data, and other details are publicly available at https://***/asvspoof/D3M. © 2020, CC BY.

关键词： Speech recognition

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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The devil is in the channels: Mutual-channel loss for fine-grained image classification

arXiv

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arXiv 2020年

作者： Chang, Dongliang Ding, Yifeng Xie, Jiyang Bhunia, Ayan Kumar Li, Xiaoxu Ma, Zhanyu Wu, Ming Guo, Jun Song, Yi-Zhe Pattern Recognition and Intelligent System Laboratory School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China Centre for Vision Speech and Signal Processing University of Surrey London United Kingdom

The key to solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex networks designed specifically to learn part-level discriminate feature representations. In this paper, we show that it is possible to cultivate subtle details without the need for overly complicated network designs or training mechanisms- a single loss is all it takes. The main trick lies with how we delve into individual feature channels early on, as opposed to the convention of starting from a consolidated feature map. The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component. The discriminality component forces all feature channels belonging to the same class to be discriminative, through a novel channel-wise attention mechanism. The diversity component additionally constraints channels so that they become mutually exclusive across the spatial dimension. The end result is therefore a set of feature channels, each of which reflects different locally discriminative regions for a specific class. The MC-Loss can be trained end-to-end, without the need for any boundingbox/ part annotations, and yields highly discriminative regions during inference. Experimental results show our MC-Loss when implemented on top of common base networks can achieve stateof- the-art performance on all four fine-grained categorization datasets (CUB-Birds, FGVC-Aircraft, Flowers-102, and Stanford Cars). Ablative studies further demonstrate the superiority of the MC-Loss when compared with other recently proposed generalpurpose losses for visual classification, on two different base networks. Code available at https://***/dongliangchang/ Mutual-Channel-Loss. Copyright © 2020, The Authors. All rights reserved.

关键词： image classification

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Dysfunction and recovery of the cortical connectome gradient and its association with gene expression profiles in methamphetamine and heroin use disorders

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Progress in Neuro-Psychopharmacology and Biological Psychiatry 2025年 139卷 111391页

作者： Du, Zhe Yang, Wenhan Wen, Xinwen Cai, Suping Liu, Jun Yuan, Kai School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China Department of Radiology Second Xiangya Hospital Central South University Changsha 410011 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou 014010 China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'an China Ganzhou City Key Laboratory of Mental Health The Third People's Hospital of Ganzhou City Jiangxi Ganzhou 341000 China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China

Background: The hierarchy and segregation of community-based brain networks can be characterized by functional connectome gradient (FCG). Whether the cortical FCG was disrupted and could even be reversed after prolonged abstinence in substance use disorders (SUDs) remained unclear. Less is known about the underlying genetic basis. Methods: We utilized three independent datasets (n = 247) to investigate cortical FCG dysfunction and recovery after prolonged abstinence in methamphetamine use disorder (MUD) (n = 131), and validated in heroin use disorder (HUD) (n = 36). Furthermore, magnetic resonance spectroscopy (MRS) was employed to test the relationship between the glutamate in the ventral tegmental area (VTA) and cortical FCG. Postmortem gene expression was utilized to assess the transcriptional profiles related to cortical FCG alterations. Support vector regression (SVR) model based on the baseline cortical FCG was built to predict craving changes after long-term abstinence. Results: MUD and HUD individuals exhibited similar cortical FCG dysfunction and recovery at global and network levels, mainly in dorsal and ventral attentions network (DAN and VAN), frontoparietal network (FPN) and default mode network (DMN). The glutamate levels in VTA were correlated with the gradient distance between FPN and limbic network (LIM). The transcriptional profiles explained variance of the altered gradient pattern, with the most related genes enriched in synaptic signaling. Baseline cortical FCG significantly predicted craving changes after long-term abstinence. Conclusions: We provided novel insights into the understanding of SUD as treatable health conditions from cortical FCG changes, which could be considered as the potential biomarkers of craving changes after prolonged abstinence for SUDs. © 2025 Elsevier Inc.

关键词： Cortical functional connectome gradient Gene expression Glutamate Heroin use disorder Methamphetamine use disorder Support vector regression

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Road segmentation for remote sensing images using adversarial spatial pyramid networks

arXiv

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arXiv 2020年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Wang, Ruili Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom School of Logistics and Transportation Central South University of Forestry and Technology China School of Natural and Computational Sciences Massey University Auckland New Zealand

To read the paper please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or high costs of manual annotation. To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. We incorporate a feature pyramid network into generative adversarial networks to minimize the difference between the source and target domains. A generator is learned to produce quality synthetic images, and the discriminator attempts to distinguish them. We also propose a feature pyramid network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales’ objects. Indeed, a novel scale-wise architecture is introduced to learn from the multi-level feature maps and improve the semantics of the features. For optimization, the model is trained by a joint reconstruction loss function, which minimizes the difference between the fake images and the real ones. A wide range of experiments on three datasets prove the superior performance of the proposed approach in terms of accuracy and efficiency. In particular, our model achieves state-of-the-art 78.86 IOU on the Massachusetts dataset with 14.89M parameters and 86.78B FLOPs, with 4× fewer FLOPs but higher accuracy (+3.47% IOU) than the top performer among state-of-the-art approaches used in the evaluation. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A novel deep structure U-net for sea-land segmentation in remote sensing images

arXiv

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arXiv 2020年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Wang, Ruili Zhou, Huiyu Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China School of Logistics and Transportation Central South University of Forestry and Technology China School of Natural and Computational Sciences Massey University Auckland New Zealand Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sea–land segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and lands. Although several Convolutional Neural Networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each down- and up-sampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multi-scale contextual information. Each dense network block contains multilevel convolution layers, short-range connections and an identity mapping connection which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results whilst minimizing computational costs. We have performed extensive experiments on two real datasets Google-Earth and ISPRS and compare the proposed RDU-Net against several variations of Dense Networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Automatic Monitoring of Driver's Physiological Parameters Based on Microarray Camera

Automatic Monitoring of Driver's Physiological Parameters Ba...

引用

IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Jiancheng Zou Zhengzheng Li Peizhou Yan Institute of Image Processing and Pattern Recognition North China University of Technology Beijing Key Laboratory of Urban Rod Traffic Intelligent Control Technology North China University of Technology Shijingshan District Beijing China

Driver's physical and mental states are very important factors affecting the driving states. Traffic accidents are occurred by accompanying abnormal physiological parameters. So how to monitor automatically driver's physiological parameters is an important problem. A real-time driver's physiological parameters monitoring method based on microarray camera is proposed in this paper. Firstly, we use a microarray camera to acquire videos in real time without contact. Secondly, the videos are registered and fused into a high quality video. Thirdly, deep learning framework is used to capture driver's facial data quickly. Then based on the principle of imaging photoplethysmography (IPPG) algorithm, physiological parameters such as heart rate (HR) and oxygen saturation (SpO 2 ) are monitored in real time. Results of expression analysis can further support the physiological parameters monitoring results. The experimental results show that the method proposed in this paper can monitor the physiological parameters of drivers in real time and accurately, and it is hopeful to play an active role in improving traffic safety.

关键词： Biomedical monitoring Monitoring Cameras Videos Heart rate Real-time systems

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Data consistent artifact reduction for limited angle tomography with deep learning prior

arXiv

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arXiv 2019年

作者： Huang, Yixing Preuhs, Alexander Lauritsch, Günter Manhart, Michael Huang, Xiaolin Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-Universitt Erlangen-Nrnberg Erlangen91058 Germany Siemens Healthcare GmbH Forchheim91301 Germany Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Erlangen91058 Germany

Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data;b) deep learning methods are sensitive to noise. Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120° cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method. Copyright © 2019, The Authors. All rights reserved.

关键词： image enhancement

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Current Progress and Challenges in Large-scale 3D Mitochondria Instance Segmentation

TechRxiv

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TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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Unsupervised Local Discrimination for Medical images

arXiv

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arXiv 2021年

作者： Chen, Huai Wang, Renzhen Wang, Xiuying Li, Jieyu Fang, Qu Li, Hui Bai, Jianhao Peng, Qing Meng, Deyu Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Computer Science The University of Sydney SydneyNSW2006 Australia Department of Ophthalmology Shanghai Tenth People’s Hospital Tongji University Shanghai200240 China The Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China The Changchun GeneScience Pharmaceutical Co. LTD China

Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images. Specifically, this model is equipped with an embedding module for pixel-wise embedding and a clustering module for generating segmentation. And these two modules are unified through optimizing our novel region discrimination loss function in a mutually beneficial mechanism, which enables our model to reflect structure information as well as measure pixel-wise and region-wise similarity. Furthermore, based on LD, we propose a center-sensitive one-shot landmark localization algorithm and a shape-guided cross-modality segmentation model to foster the generalizability of our model. When transferred to downstream tasks, the learned representation by our method shows a better generalization, outperforming representation from 18 state-of-the-art (SOTA) methods and winning 9 out of all 12 downstream tasks. Especially for the challenging lesion segmentation tasks, the proposed method achieves significantly better performances. The source codes are publicly available at https://***/HuaiChen-1994/LDLearning. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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