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Clinical experience sharing by similar case retrieval

Neda Barzegar Marvasti, Ceyhun Burak Akgül, Burak Acar, Nadin Kökciyan, Suzan Üsküdarlı, Pınar Yolum, Rüstü Türkay, Barıs Bakır
2013 Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare - MIIRH '13  
One way of implementing CES is through the use of content based case retrieval (CBCR), where database of medical cases is browsed for case instances that are similar to the input query case.  ...  A list of CT image features serves as computer generated descriptors together with user expressed annotations collected using a novel ontology of liver for radiology (ONLIRA).  ...  The CBIR component utilizes both the image content and the other medical image metadata [1] .  ... 
doi:10.1145/2505323.2505335 dblp:conf/mm/MarvastiAAKUYTB13 fatcat:k265bfhw6rhxdbv2nc65ruzsci

A clinically motivated self-supervised approach for content-based image retrieval of CT liver images [article]

Kristoffer Knutsen Wickstrøm and Eirik Agnalt Østmo and Keyur Radiya and Karl Øyvind Mikalsen and Michael Christian Kampffmeyer and Robert Jenssen
2022 arXiv   pre-print
Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers from some critical limitations.  ...  analysis in the context of CBIR of CT liver images.  ...  Acknowledgments This work was supported by The Research Council of Norway (RCN), through its Centre for Research-based Innovation  ... 
arXiv:2207.04812v1 fatcat:2zmwuf7yynhadh6g4qvpcnve6i

Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT

Adrien Depeursinge, Camille Kurtz, Christopher Beaulieu, Sandy Napel, Daniel Rubin
2014 IEEE Transactions on Medical Imaging  
likelihood and (2) explicitly link them with pixel-based image content in the context of a given imaging domain.  ...  We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions.  ...  Acknowledgments This work was supported by the Swiss National Science Foundation (PBGEP2_142283), and the National Cancer Institute, National Institutes of Health (U01-CA-142555 and R01-CA-160251).  ... 
doi:10.1109/tmi.2014.2321347 pmid:24808406 pmcid:PMC4129229 fatcat:y32sv3lkdndjpox6phsi6t6q24

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
2019 Computers in Biology and Medicine  
and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems.  ...  This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  ... 
doi:10.1016/j.compbiomed.2019.02.017 pmid:31054502 pmcid:PMC6531364 fatcat:tcyorm6g3ff6dg7ty2ubtqorjq

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
surgery 311 Spatiotemporal Manifold Prediction Model for Anterior Vertebral Body Growth Modulation Surgery in Idiopathic Scoliosis 312 Order-Sensitive Deep Hashing for Multimorbidity Medical Image Retrieval  ...  for Histopathology 351 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images 352 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

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 majority of the methods already in use in CBMIR enhance the retrieval of a medical image and diseases diagnosis by reducing the issue of the semantic gap between low visual and high semantic levels  ...  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.  ...  The LWP feature descriptor was tested using three CT medical image retrieval studies on three medical CT-image bases (NEMA-CT database, EXACT09-CT database, TCIA-CT databases), and compared LWP with LTP  ... 
doi:10.47760/ijcsmc.2022.v11i03.008 fatcat:656cjypw75h43mjfflbngdouia

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

Ceyhun Burak Akgül, Daniel L. Rubin, Sandy Napel, Christopher F. Beaulieu, Hayit Greenspan, Burak Acar
2010 Journal of digital imaging  
Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support.  ...  KEY WORDS: Content-based image retrieval, imaging informatics, information storage and retrieval, digital image management, decision support From the Electrical and Electronics Engineering Department,  ...  Instead, the metric used should define a convex space of semantically similar images. This calls for the concept of manifolds and manifold-learning techniques.  ... 
doi:10.1007/s10278-010-9290-9 pmid:20376525 pmcid:PMC3056970 fatcat:54efjsvb2vdxhecunbmv7pazei

Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review

Henning Muller, Devrim Unay
2017 IEEE transactions on multimedia  
This text is a systematic review of recent work (concentrating on the period between 2011-2017) on content-based multi-modal retrieval and image understanding in the medical domain, where image understanding  ...  Content-based multimedia retrieval has been an active research domain since the mid 1990s.  ...  The search terms employed were "content-based medical retrieval", "medical image retrieval", "medical visual information retrieval", "deep learning", "convolutional learning", "large scale medical retrieval  ... 
doi:10.1109/tmm.2017.2729400 fatcat:td4s7hbegzbmhlosalzlc3p7tq

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  ArXiv was searched for papers mentioning one of a set of terms related to medical imaging.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Large-scale retrieval for medical image analytics: A comprehensive review

Zhongyu Li, Xiaofan Zhang, Henning Müller, Shaoting Zhang
2018 Medical Image Analysis  
In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval.  ...  Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis.  ...  manifold learning for content-based image retrieval of prostate histopathology, 1395 in: MICCAI 2007 Workshop on Content-based Image Retrieval for Biomedical Image 1396 Archives: Achievements, Problems  ... 
doi:10.1016/j.media.2017.09.007 pmid:29031831 fatcat:s6jnxawnongufgdngpjeifv3vm

Front Matter: Volume 9035

2014 Medical Imaging 2014: Computer-Aided Diagnosis  
The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters.  ...  The CID Number appears on each page of the manuscript. The complete citation is used on the first page, and an abbreviated version on subsequent pages.  ...  Hall, Philips Research North America (United States) 9035 1N A content based framework for mass retrieval in mammograms [9035-58] S. ix Proc. of SPIE Vol. 9035 903501-9 xi Downloaded From  ... 
doi:10.1117/12.2064848 dblp:conf/micad/X14 fatcat:gsur3ldv7vadbfl3uo34ghmf7a

Conditional Generation of Medical Images via Disentangled Adversarial Inference [article]

Mohammad Havaei, Ximeng Mao, Yiping Wang, Qicheng Lao
2021 arXiv   pre-print
In this work we propose a methodology to learn from the image itself, disentangled representations of style and content, and use this information to impose control over the generation process.  ...  Current practices in using cGANs for medical image generation, only use a single variable for image generation (i.e., content) and therefore, do not provide much flexibility nor control over the generated  ...  We also demonstrate the use case of DRAI in the style or content based image retrieval.  ... 
arXiv:2012.04764v2 fatcat:kikndmwg5ng2hjxyfumuouih6a

Deep learning workflow in radiology: a primer

Emmanuel Montagnon, Milena Cerny, Alexandre Cadrin-Chênevert, Vincent Hamilton, Thomas Derennes, André Ilinca, Franck Vandenbroucke-Menu, Simon Turcotte, Samuel Kadoury, An Tang
2020 Insights into Imaging  
Key ideas will be illustrated by examples from a prototypical project on imaging of colorectal liver metastasis.  ...  Specifically, the objectives of this article are to provide an overview of clinical use cases of deep learning, describe the composition of multi-disciplinary team, and summarize current approaches to  ...  Fig. 4 Types of learning. With supervised learning, the number of inputs (CT images in this example) equals numbers of targets (malignancy status of a lesion here).  ... 
doi:10.1186/s13244-019-0832-5 pmid:32040647 pmcid:PMC7010882 fatcat:odgm3xc4bbdidlw7qfckuvn3eq

Machine learning and radiology

Shijun Wang, Ronald M. Summers
2012 Medical Image Analysis  
diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding  ...  In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist.  ...  Acknowledgments We thank Andrew Dwyer, MD, for critical review of the manuscript. This manuscript was support by the Intramural Research Program of the National Institutes of Health Clinical Center.  ... 
doi:10.1016/j.media.2012.02.005 pmid:22465077 pmcid:PMC3372692 fatcat:4ynexgzdhrev7dfqapmjpxexuu

Deep Generative Adversarial Neural Networks for Compressive Sensing (GANCS) MRI

Morteza Mardani, Enhao Gong, Joseph Y. Cheng, Shreyas S. Vasanawala, Greg Zaharchuk, Lei Xing, John M. Pauly
2018 IEEE Transactions on Medical Imaging  
of retrieved images.  ...  as well as with deep-learning-based schemes using pixel-wise training.  ...  Marcus Alley from the Radiology Department at Stanford University for setting up the infrastructure to automatically collect the dataset used in this paper.  ... 
doi:10.1109/tmi.2018.2858752 pmid:30040634 pmcid:PMC6542360 fatcat:2puigbybvbbyvev5gkmpiahery
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