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A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis

Jiashu Xu, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, 03056, Ukraine
<span title="2021-08-08">2021</span> <i title="MECS Publisher"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7hgv6dkr7vaq7mvfy3joc3nu2y" style="color: black;">International Journal of Image Graphics and Signal Processing</a> </i> &nbsp;
So, more and more researchers are trying to utilize SSL methods for medical image analysis, to meet the challenge of assembling large medical datasets.  ...  To our knowledge, so far there still a shortage of reviews of self-supervised learning methods in the field of medical image analysis, our work of this article aims to fill this gap and comprehensively  ...  Acknowledgment This research has been partially supported by China Scholarship Council (CSC), and Special thanks should go to my supervisor professor Sergii Stirenko, for his instructive advice and useful  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijigsp.2021.04.03">doi:10.5815/ijigsp.2021.04.03</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ff7ybaplqncthgswf3zy7cbeza">fatcat:ff7ybaplqncthgswf3zy7cbeza</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210826160540/http://www.mecs-press.org/ijigsp/ijigsp-v13-n4/IJIGSP-V13-N4-3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/97/3b/973bdcf1f0ec39a98133fdd3243e95f5367a653a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijigsp.2021.04.03"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors [article]

Lulu Tang, Ke Chen, Chaozheng Wu, Yu Hong, Kui Jia, Zhixin Yang
<span title="2020-09-17">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manner.  ...  Owing to explicitly encoding local shape manifolds in favor of semantic analysis, the proposed geometric self-supervised and privileged learning algorithms can achieve superior performance to their backbone  ...  signals to improve 3D semantic analysis. • A novel geometric self-supervised learning method is proposed to jointly encode feature discriminative for semantic analysis on point sets and also well fitting  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.04803v2">arXiv:2001.04803v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xjytkcufvzftpgtfzfi7uu7kre">fatcat:xjytkcufvzftpgtfzfi7uu7kre</a> </span>
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Spherical Transformer: Adapting Spherical Signal to CNNs [article]

Haikuan Du and Hui Cao and Shen Cai and Junchi Yan and Siyu Zhang
<span title="2021-01-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
For 3D object classification, we further propose a rendering-based projection method to improve the performance and a rotational-equivariant model to improve the anti-rotation ability.  ...  We evaluate our approach on the tasks of spherical MNIST recognition, 3D object classification and omnidirectional image semantic segmentation.  ...  Acknowledgements The work is supported by NSFC 61703092, and the foundation (AI2020003) of Key Laboratory of Artificial Intel-  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.03848v2">arXiv:2101.03848v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qetezjqhpfgx5lx3pi4apuxgju">fatcat:qetezjqhpfgx5lx3pi4apuxgju</a> </span>
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Tangent Convolutions for Dense Prediction in 3D

Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</a> </i> &nbsp;
Using tangent convolutions, we design a deep fully-convolutional network for semantic segmentation of 3D point clouds, and apply it to challenging real-world datasets of indoor and outdoor 3D environments  ...  We present an approach to semantic scene analysis using deep convolutional networks. Our approach is based on tangent convolutions -a new construction for convolutional networks on 3D data.  ...  There is a variety of more exotic deep learning formulations for 3D analysis that do not address large-scale semantic segmentation of whole scenes but provide interesting ideas. Yi et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2018.00409">doi:10.1109/cvpr.2018.00409</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/TatarchenkoPKZ18.html">dblp:conf/cvpr/TatarchenkoPKZ18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fbrp5q3syfdbtfqr2evhm756sa">fatcat:fbrp5q3syfdbtfqr2evhm756sa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190623133814/http://openaccess.thecvf.com/content_cvpr_2018/papers/Tatarchenko_Tangent_Convolutions_for_CVPR_2018_paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d2/4f/d24f7e0673cc4caaf0e802a66f0478d983a875e0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2018.00409"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Exploring Deep 3D Spatial Encodings for Large-Scale 3D Scene Understanding [article]

Saqib Ali Khan, Yilei Shi, Muhammad Shahzad, Xiao Xiang Zhu
<span title="2020-11-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
method for 3D scene understanding.  ...  Semantic segmentation of raw 3D point clouds is an essential component in 3D scene analysis, but it poses several challenges, primarily due to the non-Euclidean nature of 3D point clouds.  ...  method for 3D scene understanding.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.14358v1">arXiv:2011.14358v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/twqevtuvcrfulgvsis3v62pyhe">fatcat:twqevtuvcrfulgvsis3v62pyhe</a> </span>
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What is ICIDM?

<span title="2020-12-14">2020</span> <i title="IEEE"> 2020 6th International Conference on Interactive Digital Media (ICIDM) </i> &nbsp;
• Interaction design • Forensics signal analysis • Radar and array processing • Seismic signal processing • Augmented/ Mixed Reality • Animation Compression and Transmission • Semantics for Virtual  ...  Adaptive and personalized interfaces • Analysis and design methods • Architectures for interaction • Computer-based learning • Ecological interfaces • Emotions in HCI • Evaluation methods and techniques  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icidm51048.2020.9339636">doi:10.1109/icidm51048.2020.9339636</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ewxf7a2dx5fb3a3atcfsigfqxu">fatcat:ewxf7a2dx5fb3a3atcfsigfqxu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210208093534/https://ieeexplore.ieee.org/ielx7/9339608/9339597/09339636.pdf?tp=&amp;arnumber=9339636&amp;isnumber=9339597&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8f/c6/8fc660f4296d0b83aa7475cdd382ff79d315e595.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icidm51048.2020.9339636"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Tangent Convolutions for Dense Prediction in 3D [article]

Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou
<span title="2018-07-06">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Using tangent convolutions, we design a deep fully-convolutional network for semantic segmentation of 3D point clouds, and apply it to challenging real-world datasets of indoor and outdoor 3D environments  ...  We present an approach to semantic scene analysis using deep convolutional networks. Our approach is based on tangent convolutions - a new construction for convolutional networks on 3D data.  ...  There is a variety of more exotic deep learning formulations for 3D analysis that do not address large-scale semantic segmentation of whole scenes but provide interesting ideas. Yi et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.02443v1">arXiv:1807.02443v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wrjnz2nzxzhcbbuuu2ijzaywdm">fatcat:wrjnz2nzxzhcbbuuu2ijzaywdm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826085250/https://arxiv.org/pdf/1807.02443v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/06/e0/06e027d64c0386edb771dc8af8806cccf04a467d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.02443v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Polarimetric SAR Image Semantic Segmentation with 3D Discrete Wavelet Transform and Markov Random Field

Haixia Bi, Lin Xu, Xiangyong Cao, Yong Xue, Zongben Xu
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation.  ...  Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications.  ...  complex learning process.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2020.2992177">doi:10.1109/tip.2020.2992177</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xnrsfb5gvvdufnb67kf4mlpsgy">fatcat:xnrsfb5gvvdufnb67kf4mlpsgy</a> </span>
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Neural Network-Based Dynamic Segmentation and Weighted Integrated Matching of Cross-Media Piano Performance Audio Recognition and Retrieval Algorithm

Tianshu Wang, Gengxin Sun
<span title="2022-05-13">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3wwzxqpotbc73bzpemzybzg7ee" style="color: black;">Computational Intelligence and Neuroscience</a> </i> &nbsp;
The 3D convolutional neural network process is separated to compress the network parameters and improve the computational speed.  ...  This paper implements the data collection and processing, audio recognition, and retrieval algorithm for cross-media piano performance big data through three main modules: the collection, processing, and  ...  range of the speech signal. (3) According to the short-time smooth characteristics of the voice signal to the voice signal for framing processing, set the frame length for 25 ms, the towel shift for 10  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/9323646">doi:10.1155/2022/9323646</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35602641">pmid:35602641</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9122679/">pmcid:PMC9122679</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lmgefjsmdzagdekurjwqs6xwju">fatcat:lmgefjsmdzagdekurjwqs6xwju</a> </span>
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PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding [article]

Peng Yin, Lingyun Xu, Ziyue Feng, Anton Egorov, Bing Li
<span title="2021-08-27">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To tackle these challenges, we present PSE-Match, a viewpoint-free place recognition method based on parallel semantic analysis of isolated semantic attributes from 3D point-cloud models.  ...  Accurate localization on autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments where high-accurate GPS  ...  In Section II, we conduct a survey for 3D place recognition approaches and semantic-enhanced place recognition methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.00552v2">arXiv:2108.00552v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xzke2ywabng35eve3sxdiiilfu">fatcat:xzke2ywabng35eve3sxdiiilfu</a> </span>
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Template-Based and Template-Free Approaches in Cellular Cryo-Electron Tomography Structural Pattern Mining [chapter]

Xindi Wu, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Xiangrui Zeng, Zhenxi Zhu, Xin Gao, Min Xu, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA, Beijing University of Posts and Telecommunications, Beijing, China, King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia, Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
<span title="2019-11-01">2019</span> <i title="Codon Publications"> Computational Biology </i> &nbsp;
fashion for further biomedical analysis and interpretation.  ...  This chapter presents three major Cryo-ET structural pattern mining approaches to give an overview of traditional methods and recent advances in Cryo-ET data analysis.  ...  is a 3D semantic segmentation model for Cryo-ET data based on the U-Net architecture. 3D ConvNet predicts the segmentation mask of ribosomes, membrane and membrane-bound ribosomes in a multi-class fashion  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15586/computationalbiology.2019.ch11">doi:10.15586/computationalbiology.2019.ch11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/klvrpw5fgjeyfl6jvzwie7qs24">fatcat:klvrpw5fgjeyfl6jvzwie7qs24</a> </span>
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Machine Learning Paradigms for Modeling Spatial and Temporal Information in Multimedia Data Mining

Djamel Bouchaffra, Abbes Amira, Ce Zhu, Chu-Song Chen
<span title="">2010</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gdgab5ivavcfro52kek2ipdpfi" style="color: black;">Advances in Artificial Intelligence</a> </i> &nbsp;
These models should be able to bridge the gap between low-level audiovisual features which require signal processing and high-level semantics.  ...  It has the ability to discover and compare stable patterns in a RFID signal, and is appropriate for continuous learning.  ...  These models should be able to bridge the gap between low-level audiovisual features which require signal processing and high-level semantics.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2010/312350">doi:10.1155/2010/312350</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ahrarqlanjehnnviojdzfjlsve">fatcat:ahrarqlanjehnnviojdzfjlsve</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812223205/http://bura.brunel.ac.uk/bitstream/2438/5619/1/Fulltext.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/89/8d/898dd32b2f53e9943915d62b2129a80d0173513c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2010/312350"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Micro Computed Tomography Analysis of Four-Way Conversion Catalysts using Artificial Intelligence-Enabled Image Processing

Robert Palomino, Ke-Bin Low, Chunxin Ji, Ivan Petrovic, Florian Waltz, Thomas Schmitz
<span title="2021-07-30">2021</span> <i title="Cambridge University Press (CUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p62srgk5yvb2lpsg64xdrhwdyu" style="color: black;">Microscopy and Microanalysis</a> </i> &nbsp;
Significance Artificial intelligence-driven computer vision enables robust semantic segmentation for tomographic data that enhances 3D structural elucidation and paves the way for quantitative analysis  ...  Quantitative analysis and visualization of the segmentation in 3D were done using a combination of MIPAR Image Analysis and ImageJ [3] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1017/s1431927621003883">doi:10.1017/s1431927621003883</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gumvv3gdtbb2ppgzwzghgqrxsu">fatcat:gumvv3gdtbb2ppgzwzghgqrxsu</a> </span>
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Front Matter: Volume 11878

Xudong Jiang, Hiroshi Fujita
<span title="2021-06-30">2021</span> <i title="SPIE"> Thirteenth International Conference on Digital Image Processing (ICDIP 2021) </i> &nbsp;
Publication of record for individual papers is online in the SPIE Digital Library.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  and image processing 11878 04 A hybrid CNN-LSTM network for hand gesture recognition with surface EMG signals 11878 05 A preliminary study on attitude recognition from speaker's orofacial motions using  ... 
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GWA: A Large High-Quality Acoustic Dataset for Audio Processing [article]

Zhenyu Tang, Rohith Aralikatti, Anton Ratnarajah, Dinesh Manocha
<span title="2022-04-04">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our dataset samples acoustic environments from over 6.8K high-quality diverse and professionally designed houses represented as semantically labeled 3D meshes.  ...  Moreover, we highlight the benefits of GWA on audio deep learning tasks such as automated speech recognition, speech enhancement, and speech separation.  ...  Many digital signal processing algorithms and audio deep learning techniques have been proposed to extract information from audio signals.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.01787v1">arXiv:2204.01787v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lni7ucyqivabbi2bobse5lqaxa">fatcat:lni7ucyqivabbi2bobse5lqaxa</a> </span>
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