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Individualized brain mapping for navigated neuromodulation

Chinese Medical Journal 2024年第5期137卷 508-523页

作者： Chaohong Gao Xia Wu Xinle Cheng Kristoffer Hougaard Madsen Congying Chu Zhengyi Yang Lingzhong Fan Sino–Danish College University of Chinese Academy of SciencesBeijing 100190China Brainnetome Center National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Department of Applied Mathematics and Computer Science Technical University of DenmarkKongens Lyngby 2800Denmark Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital Amager and HvidovreHvidovre 2650Denmark CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Health and Life Sciences University of Health and Rehabilitation SciencesQingdaoShandong 266000China

The brain is a complex organ that requires precise mapping to understand its structure and *** atlases provide a powerful tool for studying brain circuits,discovering biological markers for early diagnosis,and developing personalized treatments for neuropsychiatric *** techniques,such as transcranial magnetic stimulation and deep brain stimulation,have revolutionized clinical therapies for neuropsychiatric ***,the lack of fine-scale brain atlases limits the precision and effectiveness of these *** in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation ***,the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized *** article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles,advantages,disadvantages,and future trends of these *** article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.

关键词： Brain atlas Individualization Navigated neuromodulation Multimodal magnetic resonance imaging Transcranial magnetic stimulation Deep brain stimulation

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Digging into Uncertainty in Self-supervised Multi-view Stereo

Digging into Uncertainty in Self-supervised Multi-view Stere...

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International Conference on computer vision (ICCV)

作者： Hongbin Xu Zhipeng Zhou Yali Wang Wenxiong Kang Baigui Sun Hao Li Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences South China University of Technology Alibaba Group Pazhou Laboratory Shanghai AI Laboratory

ISBN: (纸本)9781665428132

Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations about the effectiveness of the pretext task in self-supervised MVS. To this end, we propose to estimate epistemic uncertainty in self-supervised MVS, accounting for what the model ignores. Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background. To address these issues, we propose a novel Uncertainty reduction Multi-view Stereo (U-MVS) framework for self-supervised learning. To alleviate ambiguous supervision in foreground, we involve extra correspondence prior with a flow-depth consistency loss. The dense 2D correspondence of optical flows is used to regularize the 3D stereo correspondence in MVS. To handle the invalid supervision in background, we use Monte-Carlo Dropout to acquire the uncertainty map and further filter the unreliable supervision signals on invalid regions. Extensive experiments on DTU and Tank&Temples benchmark show that our U-MVS framework 1 achieves the best performance among unsupervised MVS methods, with competitive performance with its supervised opponents.

关键词： Optical losses Optical filters computer vision Uncertainty Three-dimensional displays Monte Carlo methods Benchmark testing

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Digging into uncertainty in self-supervised multi-view stereo

arXiv

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arXiv 2021年

作者： Xu, Hongbin Zhou, Zhipeng Wang, Yali Kang, Wenxiong Sun, Baigui Li, Hao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences South China University of Technology Shanghai AI Laboratory Alibaba Group Pazhou Laboratory

Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations about the effectiveness of the pretext task in self-supervised MVS. To this end, we propose to estimate epistemic uncertainty in self-supervised MVS, accounting for what the model ignores. Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background. To address these issues, we propose a novel Uncertainty reduction Multi-view Stereo (UMVS) framework for self-supervised learning. To alleviate ambiguous supervision in foreground, we involve extra correspondence prior with a flow-depth consistency loss. The dense 2D correspondence of optical flows is used to regularize the 3D stereo correspondence in MVS. To handle the invalid supervision in background, we use Monte-Carlo Dropout to acquire the uncertainty map and further filter the unreliable supervision signals on invalid regions. Extensive experiments on DTU and Tank&Temples benchmark show that our U-MVS framework achieves the best performance among unsupervised MVS methods, with competitive performance with its supervised opponents. © 2021, CC BY.

关键词： Image reconstruction

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Survey on Handwritten Mathematical Expression recognition in the Last Decade: Grammar- and Graph-Based Parsing, and the Rise of Encoder-Decoder Models and Graph Neural Networks

SSRN

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SSRN 2023年

作者： Truong, Thanh-Nghia Nguyen, Cuong Tuan Zanibbi, Richard Mouchère, Harold Nakagawa, Masaki University Research Administrator Center Department of Computer Science Rochester Institute of Technology NY United States Tokyo University of Agriculture and Technology Tokyo Japan FPT University HCMC Campus Viet Nam Document and Pattern Recognition Lab Rochester Institute of Technology New York United States LS2N - UMR CNRS 6004 University of Nantes Nantes France

Machine recognition of handwritten mathematical expressions (HMEs) is an area that has attracted interest owing to steady progress in handwriting recognition and the rapid emergence of pen- and touch-based devices. HME recognition can be considered as an extension of text recognition with a two-dimensional structure of characters and symbols. This survey examines the solutions proposed for recognizing online and offline HMEs over the last decade. A common strategy for recognizing HMEs involves splitting the recognition process into four key tasks: symbol segmentation, symbol classification, spatial relation classification, and structural analysis. Recently, encoder–decoder models using Deep Neural Networks (DNNs) have become popular and can perform all key tasks simultaneously and achieve high performance. Furthermore, evaluation methods and benchmark datasets were used to explore the implicit dependencies among key tasks. Finally, we point out some limitations of the current systems and present a future outlook on the DNN approach and the utilization of context to support HMEs parsing. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Even big data is not enough: need for a novel reference modelling for forensic document authentication

Even big data is not enough: need for a novel reference mode...

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作者： Garain, Utpal Halder, Biswajit Computer Vision and Pattern Recognition Unit and Centre for Artificial Intelligence and Machine Learning Indian Statistical Institute 203 B. T. Road Kolkata700108 India Department of CSE Narula Institute of Technology Agarpara Kolkata700109 India

With the emergence of big data, deep learning (DL) approaches are becoming quite popular in many branches of science. Forensic science is no longer an exception. However, there are certain problems in forensic science where the solutions would hardly benefit from the recent advances in DL algorithms. Document authentication is one such problem where we can have many reference samples, and with the big data scenario probably we would have even more number of reference samples but number of defective or forged samples will remain an issue. Experts often encounter situations where there is no or hardly a scanty number of forged samples available. In such situation, employment of data-hungry algorithms would be inefficient as they will not be able to learn the forged samples properly. This paper addresses this problem and proposes a novel reference modelling framework for forensic document authentication. The approach is based on Mahalanobis space. Two questioned document examination problems have been studied to show the effectiveness of our reference modelling algorithm which has also been compared to a commonly used learning approach, namely neural network-based classification. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Big data

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VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection

arXiv

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arXiv 2022年

作者： Liu, Yi Zhang, Xuan Li, Ying Liang, Guixin Jiang, Yabing Qiu, Lixia Tang, Haiping Xie, Fei Yao, Wei Dai, Yi Qiao, Yu Wang, Yali ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Shenzhen Bwell Technology Co. Ltd China Shenzhen Longhua Drainage Co. Ltd China Shanghai AI Laboratory Shanghai China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Video understanding is an important problem in computer vision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed from the long videos, and a single class of human action is assumed for each clip. However, we may face more complicated scenarios in the industrial applications. For example, in the real-world urban pipe system, anomaly defects are fine-grained, multi-labeled, domain-relevant. To recognize them correctly, we need to understand the detailed video content. For this reason, we propose to advance research areas of video understanding, with a shift from traditional action recognition to industrial anomaly analysis. In particular, we introduce two high-quality video benchmarks, namely QV-Pipe and CCTV-Pipe, for anomaly inspection in the real-world urban pipe systems. Based on these new datasets, we will host two competitions including (1) Video Defect Classification on QV-Pipe and (2) Temporal Defect Localization on CCTV-Pipe. In this report, we describe the details of these benchmarks, the problem definitions of competition tracks, the evaluation metric, and the result summary. We expect that, this competition would bring new opportunities and challenges for video understanding in smart city and beyond. The details of our VideoPipe challenge can be found in https://***. Copyright © 2022, The Authors. All rights reserved.

关键词： Defects

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Description-Enhanced Label Embedding Contrastive Learning for Text Classification

arXiv

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arXiv 2023年

作者： Zhang, Kun Wu, Le Lv, Guangyi Chen, Enhong Ruan, Shulan Liu, Jing Zhang, Zhiqiang Zhou, Jun Wang, Meng School of Computer and Information Hefei University of Technology Anhui Hefei230029 China AI Lab Lenovo Research Beijing100094 China The School of Computer Science and Technology University of Science and Technology of China Hefei230026 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Ant Group CO. Ltd Hangzhou310007 China

Text Classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially Pre-trained Language Models (PLMs). Usually, these methods concentrate on input sentences and corresponding semantic embedding generation. However, for another essential component: labels, most existing works either treat them as meaningless one-hot vectors or use vanilla embedding methods to learn label representations along with model training, underestimating the semantic information and guidance that these labels reveal. To alleviate this problem and better exploit label information, in this paper, we employ Self-Supervised Learning (SSL) in model learning process and design a novel self-supervised Relation of Relation (R2) classification task for label utilization from a one-hot manner perspective. Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets. Meanwhile, triplet loss is employed to enhance the analysis of differences and connections among labels. Moreover, considering that one-hot usage is still short of exploiting label information, we incorporate external knowledge from WordNet to obtain multi-aspect descriptions for label semantic learning and extend R2-Net to a novel Description-Enhanced Label Embedding network (DELE) from a label embedding perspective. One step further, since these fine-grained descriptions may introduce unexpected noise, we develop a mutual interaction module to select appropriate parts from input sentences and labels simultaneously based on Contrastive Learning (CL) for noise mitigation. Extensive experiments on different text classification tasks reveal that R2-Net can effectively improve classification performance, and DELE can make better

关键词： Classification (of information)

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DCT-phase statistics for forged IMEI numbers and air ticket detection

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Expert Systems with Applications 2021年 164卷

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Kanchan, Swati Basavaraja, V. Guru, D.S. Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Studies in Computer Science University of Mysore Karnataka India National Key Lab for Novel Software Technology Nanjing University China Sydney Australia

New tools have been developing with the intention of having more flexibility and greater user-friendliness for editing the images and documents in digital technologies, but, unfortunately, they are also being used for manipulating and tampering information. Examples of such crimes include creating forged International Mobile Equipment Identity (IMEI) numbers which are embedded on mobile packages and inside smart mobile cases for illicit activities. Another example of such crimes is altering the name or date on air tickets for breaching security at the airport. This paper presents a new expert system for detecting forged IMEI numbers as well as altered air ticket images. The proposed method derives the phase spectrum using the Discrete Cosine Transform (DCT) to highlight the suspicious regions;it is unlike the phase spectrum from a Fourier transform, which is ineffective due to power spectrum noise. From the phase spectrum, our method extracts phase statistics to study the effect of distortions introduced by forgery operations. This results in feature vectors, which are fed to a Support Vector Machine (SVM) classifier for detection of forged IMEI numbers and air ticket images. Experimental results on our dataset of forged IMEI numbers (which is created by us for this work), on altered air tickets, on benchmark datasets of video caption text (which is tampered text), and on altered receipts of the ICPR 2018 FDC dataset, show that the proposed method is robust across different datasets. Furthermore, comparative studies of the proposed method with the existing methods on the same datasets show that the proposed method outperforms the existing methods. The dataset created will be available freely on request to the authors. © 2020 Elsevier Ltd

关键词： Discrete cosine transforms

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Interactive Multi-dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration 1

引用

16th European Conference on computer vision, ECCV 2020

作者： He, Jingwen Dong, Chao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9783030585655

ISBN: (纸本)9783030585648

Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD is setup to handle multiple degradations adaptively and relief unbalanced learning problem in different degradations. We also propose a deep architecture - CResMD with newly introduced controllable residual connections for multi-dimension modulation. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The values of these weights are generated by another condition network. We further propose a new data sampling strategy based on beta distribution to balance different degradation types and levels. With corrupted image and degradation information as inputs, the network can output the corresponding restored image. By tweaking the condition vector, users can control the output effects in MD space at test time. Extensive experiments demonstrate that the proposed CResMD achieve excellent performance on both SD and MD modulation tasks. Code is available at https://***/hejingwenhejingwen/CResMD. © 2020, Springer Nature Switzerland AG.

关键词： Modulation

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Learning to Predict Context-Adaptive Convolution for Semantic Segmentation 16th

Learning to Predict Context-Adaptive Convolution for Semanti...

引用

16th European Conference on computer vision, ECCV 2020

作者： Liu, Jianbo He, Junjun Qiao, Yu Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Hong Kong

ISBN: (纸本)9783030585945

Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can effectively improve the accuracy of semantic segmentation. However, the globally-sharing feature re-weighting vector might not be optimal for regions of different classes in the input image. In this paper, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial location of the semantic feature maps. In CaC-Net, a set of context-adaptive convolution kernels are predicted from the global contextual information in a parameter-efficient manner. When used for convolution with the semantic feature maps, the predicted convolutional kernels can generate the spatially-varying feature weighting factors capturing both global and local contextual information. Comprehensive experimental results show that our CaC-Net achieves superior segmentation performance on three public datasets, PASCAL Context, PASCAL VOC 2012 and ADE20K. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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