المؤلفون: A. A. Borisov, K. M. Arzamasov, S. S. Semenov, A. V. Vladzimirsky, Yu. A. Vasiliev, А. А. Борисов, К. М. Арзамасов, С. С. Семенов, А. В. Владзимирский, Ю. А. Васильев

المساهمون: The article was written by the author's team as a part of the research work “Development of a platform for the preparation of datasets of radiation diagnostic studies”., Данная статья подготовлена авторским коллективом в рамках научно-исследовательской работы “Разработка платформы подготовки наборов данных лучевых диагностических исследований”

المصدر: Medical Visualization; Том 28, № 2 (2024); 134-144 ; Медицинская визуализация; Том 28, № 2 (2024); 134-144 ; 2408-9516 ; 1607-0763

مصطلحات موضوعية: трансферное обучение, quality assurance, DICOM metadata, DICOM-tags, dataset generation, deep convolutional neural networks, transfer learning, контроль качества, метаданные DICOM, DICOM-теги, формирование наборов данных, глубокие сверточные нейронные сети

وصف الملف: application/pdf

Relation: https://medvis.vidar.ru/jour/article/view/1346/855; https://medvis.vidar.ru/jour/article/downloadSuppFile/1346/2115; https://medvis.vidar.ru/jour/article/downloadSuppFile/1346/2116; McDonald R.J., Schwartz K.M., Eckel L.J. et al. The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad. Radiol. 2015; 22 (9): 1191–1198. https://doi.org/10.1016/j.acra.2015.05.007; van Leeuwen K.G., de Rooij M., Schalekamp S. et al. How does artificial intelligence in radiology improve efficiency and health outcomes? Pediatr. Radiol. 2022; 52 (11): 2087–2093. https://doi.org/10.1007/s00247-021-05114-8; Chetlen A.L., Chan T.L., Ballard D.H. et al. Addressing Burnout in Radiologists. Acad. Radiol. 2019; 26 (4): 526–533. https://doi.org/10.1016/j.acra.2018.07.001; Hosny A., Parmar Ch., Quackenbush J. et al. Artificial intelligence in radiology. Nat. Rev. Cancer. 2018; 18 (8): 500–510. https://doi.org/10.1038/s41568-018-0016-5; Rubin D.L. Artificial Intelligence in Imaging: The Radiologist’s Role. J. Am. Coll. Radiol. 2019; 16 (9): 1309–1317. https://doi.org/10.1016/j.jacr.2019.05.036; Savadjiev P., Chong J., Dohan A. et al. Demystification of AI-driven medical image interpretation: past, present and future. Eur. Radiol. 2019; 29 (3): 1616–1624. https://doi.org/10.1007/s00330-018-5674-x; Acosta J.N., Falcone G.J., Rajpurkar P. The Need for Medical Artificial Intelligence That Incorporates Prior Images. Radiology. 2022; 304 (2): 283–288. https://doi.org/10.1148/radiol.212830; Павлов Н.А., Андрейченко А.Е., Владзимирский А.В., Ревазян А.А., Кирпичев Ю.С., Морозов С.П. Эталонные медицинские датасеты (MosMedData) для независимой внешней оценки алгоритмов на основе искусственного интеллекта в диагностике. Dig. Diagn. 2021; 2 (1): 49–66. https://doi.org/10.17816/DD60635; Willemink M.J., Koszek W.A., Hardell C. et al. Preparing Medical Imaging Data for Machine Learning. Radiology. 2020; 295 (1): 4–15. https://doi.org/10.1148/radiol.2020192224; Park S.H., Han K. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. Radiology. 2018; 286 (3): 800–809. https://doi.org/10.1148/radiol.2017171920; European Society of Radiology (ESR). What the radiologist should know about artificial intelligence – an ESR white paper. Insights. Imaging. 2019; 10 (1): 44. https://doi.org/10.1186/s13244-019-0738-2; Борисов А.А., Семенов С.С., Арзамасов К.М. Использование трансферного обучения для автоматизированного поиска дефектов на рентгенограммах органов грудной клетки. Медицинская визуализация. 2023; 27 (1): 158–169. https://doi.org/10.24835/1607-0763-1243; Juszczyk J., Badura P., Czajkowska J. et al. Automated size-specific dose estimates using deep learning image processing. Medical Image Analysis. 2021; 68: 101898. https://doi.org/10.1016/j.media.2020.101898; Keshavamurthy K.N., Elnajjar P., El-Rowmeim A. et al. Application of Deep Learning Techniques for Characterization of 3D Radiological Datasets – A Pilot Study for Detection of Intravenous Contrast in Breast MRI. Proc. SPIE Int. Soc. Opt. Eng. 2019; 10954: 109540X. https://doi.org/10.1117/12.2513809; DICOM standart // URL: https://www.dicomstandard.org/ (дата обращения 10.01.2023); CheXpert Dataset //URL: https://stanfordmlgroup.github.io/competitions/chexpert/ (дата обращения 23.12.2022); Chest X-rays dataset // URL: https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university (дата обращения 26.12.2022); Chest X-Ray Images (Pneumonia)// URL: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia (дата обращения 20.12.2022); NIH ChestX-ray14 //URL: https://nihcc.app.box.com/v/ChestXray-NIHCC (дата обращения 20.12.2022); Han B., Du J., Jia Y. et al. Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network. J. Healthc. Eng. 2021; 2021: 5551520. https://doi.org/10.1155/2021/5551520; Karacı A. VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm. Neural. Comput. Appl. 2022; 34 (10): 8253–8274. https://doi.org/10.1007/s00521-022-06918-x; ROC-инструмент ГБУЗ НПКЦ ДиТ ДЗМ // URL: https://roc-analysis.mosmed.ai/; Mustra M., Delac K., Grgic M. et al. Overview of the DICOM standard. ELMAR, 2008. 50th International Symposium. Zadar, Croatia: 39–44. ISBN 978-1-4244-3364-3; Gueld M.O., Kohnen M., Keysers D. et al. Quality of DICOM header information for image categorization. Proc. SPIE 4685. Medical Imaging 2002: PACS and Integrated Medical Information Systems: Design and Evaluation. https://doi.org/10.1117/12.467017; Santosh K.C., Wendling L. Angular relational signature-based chest radiograph image view classification. Med. Biol. Eng. Comput. 2018; 56 (8): 1447–1458. https://doi.org/10.1007/s11517-018-1786-3; Urinbayev K., Orazbek Y., Nurambek Y. et al. End-to-End Deep Diagnosis of X-ray Images. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. https://doi.org/10.1109/EMBC44109.2020.9175208; https://medvis.vidar.ru/jour/article/view/1346

الاتاحة: https://medvis.vidar.ru/jour/article/view/1346
https://doi.org/10.24835/1607-0763-1346

المؤلفون: Ferenc Nagy, Aron K. Krizsan, Kornél Kukuts, Melinda Szolikova, Zsolt Hascsi, Sandor Barna, Antonietta Acs, Peter Szabo, Lajos Tron, Laszlo Balkay, Magnus Dahlbom, Mihaly Zentai, Attila Forgacs, Ildiko Garai

المصدر: EJNMMI Physics, Vol 8, Iss 1, Pp 1-13 (2021)

مصطلحات موضوعية: 18F-FDG PET/CT, 99mTc-MDP Bone Scan, DICOM Metadata, Protocol compliance, Quality management, Medical physics. Medical radiology. Nuclear medicine, R895-920

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2197-7364

URL الوصول: https://doaj.org/article/5369d34b71dc47cf98292b1d8d881dd8

المؤلفون: Alves, Diana, Santos, Milton, Rocha, Nelson

المصدر: ROENTGEN-Scientific Journal of Radiological Techniques; Vol. 3 No. 2 (2022): Radiological Protection; 53-62 ; ROENTGEN-Revista Científica das Técnicas Radiológicas; v. 3 n. 2 (2022): Proteção Radiológica; 53-62 ; 2184-7657

مصطلحات موضوعية: Medical Imaging, DICOM metadata, Dicoogle, Healthcare Quality, Imagiologia, Metadados DICOM, Qualidade de prestação de cuidados

وصف الملف: application/pdf

Relation: https://roentgen.pt/index.php/Principal/article/view/84/69; https://roentgen.pt/index.php/Principal/article/view/84

الاتاحة: https://roentgen.pt/index.php/Principal/article/view/84
https://doi.org/10.46885/roentgen.v3i2.84

المؤلفون: Nagy, Ferenc, Krizsan, Aron K., Kukuts, Kornél, Szolikova, Melinda, Hascsi, Zsolt, Barna, Sandor, Acs, Antonietta, Szabo, Peter, Tron, Lajos, Balkay, Laszlo, Dahlbom, Magnus, Zentai, Mihaly, Forgacs, Attila, Garai, Ildiko

المصدر: EJNMMI Physics
EJNMMI Physics, Vol 8, Iss 1, Pp 1-13 (2021)

مصطلحات موضوعية: lcsh:Medical physics. Medical radiology. Nuclear medicine, 99mTc-MDP Bone Scan, lcsh:R895-920, 18F-FDG PET/CT, Protocol compliance, DICOM Metadata, Quality management, Original Research

URL الوصول: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::31c7728df1d7026b5cfd3afb5135e868
http://europepmc.org/articles/PMC7973342

المؤلفون: Alves, Diana, Santos, Milton, Rocha, Nelson

مصطلحات موضوعية: Medical Imaging, DICOM metadata, Dicoogle, Healthcare Quality, Imagiologia, Metadados DICOM, Dicoogle, Qualidade de prestação de cuidados

وصف الملف: application/pdf

Relation: https://roentgen.pt/index.php/Principal/article/view/84; https://roentgen.pt/index.php/Principal/article/view/84/69

المؤلفون: Pšurný, Michal

Thesis Advisors: Kolář, Radim, Harabiš, Vratislav

مصطلحات موضوعية: big data analysis, big data, information from dicom, dicom, big data analysis in healthcare, dicom metadata, big data analýza, big data ve zdravotnictví, big data in healthcare, dicom tag, informace z dicom, big healthcare data, metadata

الاتاحة: http://www.nusl.cz/ntk/nusl-316821

المؤلفون: Pšurný, Michal

المساهمون: Harabiš, Vratislav, Kolář, Radim

مصطلحات موضوعية: dicom, big data, big data ve zdravotnictví, metadata, dicom tag, informace z dicom, big data analýza, big data in healthcare, big healthcare data, big data analysis, big data analysis in healthcare, dicom metadata, information from dicom

وصف الملف: application/pdf; application/zip; text/html

Relation: PŠURNÝ, M. Big data analýzy a statistické zpracování metadat v archivu obrazové zdravotnické dokumentace [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2017.; 102377; http://hdl.handle.net/11012/65456

الاتاحة: http://hdl.handle.net/11012/65456

المؤلفون: Pšurný, Michal

المساهمون: Harabiš, Vratislav, Kolář, Radim

المصدر: PŠURNÝ, M. Big data analýzy a statistické zpracování metadat v archivu obrazové zdravotnické dokumentace [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2017.

مصطلحات موضوعية: dicom, big data, big data ve zdravotnictví, metadata, dicom tag, informace z dicom, big data analýza, big data in healthcare, big healthcare data, big data analysis, big data analysis in healthcare, dicom metadata, information from dicom

وصف الملف: text/html

Relation: http://hdl.handle.net/11012/65456

الاتاحة: http://hdl.handle.net/11012/65456

المؤلفون: Milton Santos, Augusto Silva, Nelson Pacheco da Rocha, Luís Bastião

المصدر: Procedia Computer Science. :355-361

مصطلحات موضوعية: medicine.medical_specialty, Computer science, Radiography, Population, Context (language use), 02 engineering and technology, computer.software_genre, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, DICOM, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, medicine, Medical physics, Computed radiography, education, General Environmental Science, Digital radiography, DICOM metadata, education.field_of_study, Modality (human–computer interaction), Population Characterization, business.industry, Metadata, General Earth and Planetary Sciences, 020201 artificial intelligence & image processing, business, Radiology, computer, Data integration

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::798725471257405fe07066773663262b

المؤلفون: Harabiš, Vratislav, Kolář, Radim, Pšurný, Michal

مصطلحات الفهرس: dicom, big data, big data ve zdravotnictví, metadata, dicom tag, informace z dicom, big data analýza, big data in healthcare, big healthcare data, big data analysis, big data analysis in healthcare, dicom metadata, information from dicom, Text

URL: http://hdl.handle.net/11012/65456

المؤلفون: Harabiš, Vratislav, Kolář, Radim, Pšurný, Michal

مصطلحات الفهرس: dicom, big data, big data ve zdravotnictví, metadata, dicom tag, informace z dicom, big data analýza, big data in healthcare, big healthcare data, big data analysis, big data analysis in healthcare, dicom metadata, information from dicom, Text

URL: http://hdl.handle.net/11012/65456

المؤلفون: Harabiš, Vratislav, Kolář, Radim

مصطلحات الفهرس: dicom, big data, big data ve zdravotnictví, metadata, dicom tag, informace z dicom, big data analýza, big data in healthcare, big healthcare data, big data analysis, big data analysis in healthcare, dicom metadata, information from dicom, Text

URL: http://hdl.handle.net/11012/65456

المؤلفون: Harabiš, Vratislav, Kolář, Radim

مصطلحات الفهرس: dicom, big data, big data ve zdravotnictví, metadata, dicom tag, informace z dicom, big data analýza, big data in healthcare, big healthcare data, big data analysis, big data analysis in healthcare, dicom metadata, information from dicom, Text

URL: http://hdl.handle.net/11012/65456

المؤلفون: Harabiš, Vratislav, Kolář, Radim

مصطلحات الفهرس: dicom, big data, big data ve zdravotnictví, metadata, dicom tag, informace z dicom, big data analýza, big data in healthcare, big healthcare data, big data analysis, big data analysis in healthcare, dicom metadata, information from dicom, Text

URL: http://hdl.handle.net/11012/65456

المؤلفون: Harabiš, Vratislav, Kolář, Radim, Pšurný, Michal

مصطلحات الفهرس: dicom, big data, big data ve zdravotnictví, metadata, dicom tag, informace z dicom, big data analýza, big data in healthcare, big healthcare data, big data analysis, big data analysis in healthcare, dicom metadata, information from dicom, Text

URL: http://hdl.handle.net/11012/65456