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Convolutional neural networks and hash learning for feature extraction and of fast retrieval of pulmonary nodules

Pinle Qin, Jun Chen, Kai Zhang, Rui Chai
2018 Computer Science and Information Systems  
Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features information for medical image retrieval.  ...  This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems.  ...  In order to promote the production and management of such large-scale medical image data warehouse, many content-based medical image retrieval (CBMIR) methods have been proposed. M. Mizotin et al.  ... 
doi:10.2298/csis171210020q fatcat:t7hons6mjfevncwqaomsx3pqxq

EuSoMII Virtual Annual Meeting 2020 Book of Abstracts

2021 Insights into Imaging  
Short Summary: Imaging COVID-19 AI initiative is a large collaborative effort to develop a deep learning solution for assisted diagnosis of COVID-19 on CT scans, and for assessing disease severity by quantification  ...  Short Summary: Artificial Intelligence (AI) is rapidly changing medical imaging.  ...  Keywords: Mobile apps, educational tools S6 BOOK OF ABSTRACTS 24 OCTOBER, 2020 SS 7 Conclusion: The evaluation of deep learning algorithms for medical imaging should more closely emulate the  ... 
doi:10.1186/s13244-021-00975-x pmid:33759029 fatcat:66vyf7npqvfnpbdiqbozrk4pda

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs

Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik, Gayle E. Woloschak
2017 PLoS ONE  
Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections.  ...  In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained  ...  Acknowledgments The authors thank courtesy of TM Deserno, Dep. of Medical Informatics, RWTH Aachen, Germany, for providing IRMA dataset.  ... 
doi:10.1371/journal.pone.0181707 pmid:28771497 pmcid:PMC5542646 fatcat:vseqnumxhncafpz2gmpneb6opi

Deep Residual Hashing [article]

Sailesh Conjeti, Abhijit Guha Roy, Amin Katouzian, Nassir Navab
2016 arXiv   pre-print
Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks.  ...  In this paper, for the first time, we propose a deep architecture for supervised hashing through residual learning, termed Deep Residual Hashing (DRH), for an end-to-end simultaneous representation learning  ...  The existing hashing methods proposed for ef- ficient encoding and searching approaches have been proposed for large scale retrieval in machine learning and medical image computing can be categorised into  ... 
arXiv:1612.05400v1 fatcat:5gao7f3vqzcn5cvwv2jn4snzki

A Decade Survey of Content Based Image Retrieval using Deep Learning [article]

Shiv Ram Dubey
2020 arXiv   pre-print
The content based image retrieval aims to find the similar images from a large scale dataset against a query image.  ...  This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval.  ...  al. (2019) TABLE 1 : 1 The summary of large-scale datasets for deep learning based image retrieval.  ... 
arXiv:2012.00641v1 fatcat:2zcho2szpzcc3cs6uou3jpcley

Effective of Modern Techniques on Content-Based Medical Image Retrieval: A Survey

Metwally Rashad, Sameer Nooh, Ibrahem Afifi, Mohamed Abdelfatah
2022 International journal of computer science and mobile computing  
The advancement in medical imaging has resulted in a rapid and large increase in medical images inside repositories.  ...  This implies that a precise, efficient way of indexing and retrieving biomedical images is necessary to obtain medical images from such repositories in real-time.  ...  Sample images for each group are shown in Fig. 16 . Note that the Brain-Tumor database is a large-scale database. R.  ... 
doi:10.47760/ijcsmc.2022.v11i03.008 fatcat:656cjypw75h43mjfflbngdouia

Class-driven Content-Based Medical Image Retrieval using Hash Codes of Deep Features

Şaban ÖZTÜRK
2020 GAZI UNIVERSITY JOURNAL OF SCIENCE  
The widespread use of medical imaging devices has been instrumental in saving lives by allowing early diagnosis of many diseases. These medical images are stored in large databases for many purposes.  ...  Highlights • An effective and short hash code technique is provided for content-based medical image retrieval. • Manhattan distance based Siamese network activation functions are changed to hyperbolic  ...  CONCLUSION In this study, an effective hash code generation method is presented for medical datasets containing a large number of samples.  ... 
doi:10.35378/gujs.710730 fatcat:amcqqaq7m5a6hlenyeza5l3c7a

Vision Transformer Hashing for Image Retrieval [article]

Shiv Ram Dubey, Satish Kumar Singh, Wei-Ta Chu
2022 arXiv   pre-print
The proposed VTS model is fine tuned for hashing under six different image retrieval frameworks, including Deep Supervised Hashing (DSH), HashNet, GreedyHash, Improved Deep Hashing Network (IDHN), Deep  ...  Deep learning has shown a tremendous growth in hashing techniques for image retrieval. Recently, Transformer has emerged as a new architecture by utilizing self-attention without convolution.  ...  However, the TransHash is not able to utilize the pre-training of Vision Transformers over large-scale dataset and the recently investigated improved objective functions for image hashing.  ... 
arXiv:2109.12564v2 fatcat:6dy2zvoasbdlbjoiguyzd2irrm

Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion [article]

Yang Wang
2020 arXiv   pre-print
Recently, deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data.  ...  Substantial empirical studies are carried out to demonstrate its advantages that are benefited from deep multi-modal methods, which can essentially deepen the fusion from multi-modal deep feature spaces  ...  A lot of deep multi-modal hashing models [145, 158] designed feature descriptors into a similarity preserving hamming space to perform large-scale retrieval. For example, Yan et al.  ... 
arXiv:2006.08159v1 fatcat:g4467zmutndglmy35n3eyfwxku

Intelligent computational techniques for multimodal data

Shishir Kumar, Prabhat Mahanti, Su-Jing Wang
2019 Multimedia tools and applications  
hashing, High-dimensional multimedia classification, Deep CNN and extended residual units, particle swarm optimization, Cyberbullying detection on social multimedia, Multimedia detection algorithm of  ...  This guest editorial introduces the special issue on "Intelligent Computational Techniques for Multimodal Data".  ...  Conclusion We hope these contributions will be of interest and value to readers from a wide range of subject areas and form a reference for future development.  ... 
doi:10.1007/s11042-019-07936-z fatcat:icmwanpmgbd77cdv47nminn4ee

Deep learning applications and challenges in big data analytics

Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic
2015 Journal of Big Data  
A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized  ...  Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology.  ...  tagging that is useful for image indexing and retrieval.  ... 
doi:10.1186/s40537-014-0007-7 fatcat:65mi6dnv5rg6poesotupqbsm7y

Greedy Learning of Deep Boltzmann Machine (GDBM)'s Variance and Search Algorithm for Efficient Image Retrieval

Mudhafar Jalil Jassim Ghrabat, Guangzhi Ma, Hong Liu, Zaid Ameen Abduljabbar, Mustafa A.Al Sibahee, Safa Jalil Jassim
2019 IEEE Access  
Finally, the relevant features are utilized for the greedy learning of deep Boltzmann machine classifier (GDBM).  ...  INDEX TERMS Image retrieval, image preprocessing, image feature, shape feature, SIFT descriptor, Boltzmann machine classification.  ...  Hashing based methods are widely utilized in retrieving large scale cross image models.  ... 
doi:10.1109/access.2019.2948266 fatcat:lzhyuujngvhehm54jlue2kfis4

Deep Learning for Instance Retrieval: A Survey [article]

Wei Chen, Yu Liu, Weiping Wang, Erwin Bakker, Theodoros Georgiou, Paul Fieguth, Li Liu, Michael S. Lew
2022 arXiv   pre-print
area in which improved efficiency and accuracy are needed for real-time retrieval.  ...  This abundance of content creation and sharing has introduced new challenges, particularly that of searching databases for similar content-Content Based Image Retrieval (CBIR)-a long-established research  ...  ACKNOWLEDGMENT The authors would like to thank the pioneer researchers in instance retrieval and other related fields.  ... 
arXiv:2101.11282v3 fatcat:qvodunmw4bdltcneadyt7d7h5m

Selected abstracts of "Bioinformatics: from Algorithms to Applications 2020" conference

2020 BMC Bioinformatics  
O5 A rigorous approach to pairwise distance analysis of a protein family via multi-dimensional scaling and its application to the genealogy of squalene synthase paralogues of green algae Acknowledgments  ...  incorrectly guessed contacts for each image.  ...  To fulfill this gap, here we conducted a large-scale phylogenetic study of the 3-D Cry toxins.  ... 
doi:10.1186/s12859-020-03838-2 pmid:33327929 fatcat:2t65jee32rgsnohhdwd7vbwj74

Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval [article]

Lu Wang, Jie Yang
2020 arXiv   pre-print
Due to the superiority in similarity computation and database storage for large-scale multiple modalities data, cross-modal hashing methods have attracted extensive attention in similarity retrieval across  ...  original data on account of abundant loss of information and producing suboptimal hash codes; (2) the discrete binary constraint learning model is hard to solve, where the retrieval performance may greatly  ...  His current research interests include machine learning and information retrieval with respect to learning to hash in Large-scale cross-modal similarity retrieval and visual tracking.  ... 
arXiv:2001.04625v1 fatcat:jfp4jl3i25fsraiuqa5msdu24m
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