Εμφανίζονται 1 - 20 Αποτελέσματα από 821 για την αναζήτηση '"ЗЛОКАЧЕСТВЕННЫЕ ОПУХОЛИ"', χρόνος αναζήτησης: 0,92δλ Περιορισμός αποτελεσμάτων
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    Academic Journal

    Σύνδεσμος πρόσβασης: https://elar.uspu.ru/handle/ru-uspu/50954

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    Academic Journal

    Θέμα γεωγραφικό: USPU

    Relation: Специальное образование. 2022. № 1 (65)

    Διαθεσιμότητα: https://elar.uspu.ru/handle/ru-uspu/51204

  9. 9
    Academic Journal

    Πηγή: FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology; Vol 18, No 3 (2025); 365-375 ; ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология; Vol 18, No 3 (2025); 365-375 ; 2070-4933 ; 2070-4909

    Περιγραφή αρχείου: application/pdf

    Relation: https://www.pharmacoeconomics.ru/jour/article/view/1256/635; Schaumburg F., Berli C. Challenges and proposed solutions for optical reading on point-of-need testing systems. Front Sensors. 2023; 4. https://doi.org/10.3389/fsens.2023.1327240.; Visalini S., Kanagavalli R. A comprehensive survey of pneumonia diagnosis: image processing and deep learning advancements. In: 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). https://doi.org/10.1109/ICIMIA60377.2023.10426403.; Prabha S., Gupta S., Pandey S.P. Deep learning for medical image segmentation using convolutional neural networks. In: 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC). https://doi.org/10.1109/ICOCWC60930.2024.10470841.; Das M., Sambodhi P.P., Khare A., Naik S.A. Challenges of medical text and image processing. In: 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). https://doi.org/10.1109/ASSIC55218.2022.10088402.; Choudhury S., Gowri R., Babu Sena P., Dinh-Thuan D. (Eds) Intelligent Communication, Control and Devices Proceedings of ICICCD 2020: Proceedings of ICICCD 2020. https://doi.org/10.1007/978-981-16-1510-8.; Ламоткин А.И., Корабельников Д.И., Ламоткин И.А. и др. Искусственный интеллект в здравоохранении и медицине: история ключевых событий, его значимость для врачей, уровень развития в разных странах. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2024; 17 (2): 243–50. https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.254.; Ламоткин А.И., Корабельников Д.И., Ламоткин И.А. и др. Точность предварительной диагностики злокачественных меланоцитарных опухолей кожи с помощью программы искусственного интеллекта Melanoma Check. Медицинский вестник Главного военного клинического госпиталя им. Н.Н. Бурденко. 2025; 1: 42–51. https://doi.org/10.53652/2782-1730-2025-6-1-42-51.; Zhou Z., Jin Y., Ye H., et al. Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review. BMC Med Imaging. 2024; 24 (1): 164. https://doi.org/10.1186/s12880-024-01347-9.; Корабельников Д.И., Ламоткин А.И. Эффективность применения искусственного интеллекта в клинической медицине. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2025; 18 (1): 114–24. https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.287.; Alzubaidi L., Zhang J., Humaidi A.J., et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021; 8 (1): 53. https://doi.org/10.1186/s40537-021-00444-8.; LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015; 521: 436–44. https://doi.org/10.1038/nature14539.; Ламоткин А.И., Корабельников Д.И., Ламоткин И.А. Предварительная дифференциальная диагностика доброкачественных и злокачественных опухолей из эпидермальной ткани кожи с применением программы искусственного интеллекта «Derma Onko Check». Современные проблемы здравоохранения и медицинской статистики. 2025; 2: 223–42. https://doi.org/10.24412/2312-2935-2025-2-223-242.; Ламоткин А.И., Корабельников Д.И., Олисова О.Ю., Ламоткин И.А. Эффективность предварительной дифференциальной диагностики доброкачественных и злокачественных новообразований кожи с помощью программы искусственного интеллекта Derma Onko Check. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2025; 18 (2): 261–70. https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.294.; Milletari F., Ahmadi S.A., Kroll C., et al. Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision Image Underst. 2017; 164: 92–102. https://doi.org/10.48550/arXiv.1601.07014.; Yamada M., Saito Y., Imaoka H., et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep. 2019; 9 (1): 14465. https://doi.org/10.1038/s41598-019-50567-5.; Yadav D., Rathor S. Bone fracture detection and classification using deep learning approach. In: 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC). https://doi.org/10.1109/PARC49193.2020.236611.; Rahman T., Chowdhury M.E., Khandakar A., et al. Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Appl Sci. 2020; 10 (9): 3233. https://doi.org/10.3390/app10093233.; Hamamoto R., Suvarna K., Yamada M., et al. Application of artificial intelligence technology in oncology: towards the establishment of precision medicine. Cancers. 2020; 12 (12): 3532. https://doi.org/10.3390/cancers12123532.; Asada K., Kobayashi K., Joutard S., et al. Uncovering prognosisrelated genes and pathways by multi-omics analysis in lung cancer. Biomolecules. 2020; 10: 524. https://doi.org/10.3390/biom10040524.; Kobayashi K., Bolatkan A., Shiina S., Hamamoto R. Fully-connected neural networks with reduced parameterization for predicting histological types of lung cancer from somatic mutations. Biomolecules. 2020; 10 (9): 1249. https://doi.org/10.3390/biom10091249.; Takahashi S., Asada K., Takasawa K., et al. Predicting deep learning based multi-omics parallel integration survival subtypes in lung cancer using reverse phase protein array data. Biomolecules. 2020; 10 (10): 1460. https://doi.org/10.3390/biom10101460.; Takahashi S., Sakaguchi Y., Kouno N., et al. Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. J Med Syst. 2024; 48 (1): 84. https://doi.org/10.1007/s10916-024-02105-8.; Selvaraju R.R., Cogswell M., Das A., et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 Proceedings of the IEEE international conference on computer vision. https://doi.org/10.48550/arXiv.1610.02391.; Takahashi S., Takahashi M., Kinoshita M., et al. Fine-tuning approach for segmentation of gliomas in brain magnetic resonance images with a machine learning method to normalize image differences among facilities. Cancers. 2021; 13: 1415. https://doi.org/10.3390/cancers13061415.; Nam H., Lee H., Park J., et al. Reducing domain gap by reducing style bias. In: 2021 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.1910.11645.; Yan W., Wang Y., Gu S., et al. The domain shift problem of medical image segmentation and vendor-adaptation by Unet-GAN. In: Medical Image Computing and Computer Assisted Intervention– MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II. https://doi.org/10.48550/arXiv.1910.13681.; Barzekar H., Patel Y., Tong L., Yu Z. MultiNet with transformers: a model for cancer diagnosis using images. arXiv:230109007. https://doi.org/10.48550/arXiv.2301.09007.; Vaswani A., Shazeer N., Parmar N., et al. Attention is all you need. In: Advances in Neural Information Processing Systems 30 (NIPS 2017). https://doi.org/10.48550/arXiv.1706.03762.; Dosovitskiy A., Beyer L., Kolesnikov A., et al. An image is worth 16×16 words: transformers for image recognition at scale. arXiv:201011929. https://doi.org/10.48550/arXiv.2010.11929.; Liu Y., Wu Y.H., Sun G., et al. Vision transformers with hierarchical attention. arXiv:210603180. https://doi.org/10.48550/arXiv.2106.03180.; Han K., Wang Y., Chen H., et al. A survey on vision transformer. arXiv:2012.12556. https://doi.org/10.48550/arXiv.2012.12556.; Hatamizadeh A., Yin H., Heinrich G., et al. In: 2023 Global context vision transformers. arXiv:2206.09959. https://doi.org/10.48550/arXiv.2206.09959.; He K., Gan C., Li Z., et al. Transformers in medical image analysis. Intel Med. 2023; 3 (1): 59–78. https://doi.org/10.1016/j.imed.2022.07.002.; Stassin S., Corduant V., Mahmoudi S.A., Siebert X. Explainability and evaluation of vision transformers: an in-depth experimental study. Electronics. 2023; 13 (1): 175. https://doi.org/10.3390/electronics13010175.; Chetoui M., Akhloufi M.A. Explainable vision transformers and radiomics for COVID-19 detection in chest X-rays. J Clin Med. 2022; 11 (11): 3013. https://doi.org/10.3390/jcm11113013.; Dipto S.M., Reza M.T., Rahman M.N.J., et al. An XAI integrated identification system of white blood cell type using variants of vision transformer. 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    Academic Journal

    Πηγή: FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology; Vol 18, No 3 (2025); 437-447 ; ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология; Vol 18, No 3 (2025); 437-447 ; 2070-4933 ; 2070-4909

    Περιγραφή αρχείου: application/pdf

    Relation: https://www.pharmacoeconomics.ru/jour/article/view/1255/634; Kaul V., Enslin S., Gross S.A. History of artificial intelligence in medicine. Gastrointest Endosc. 2020; 92 (4): 807–12. https://doi.org/10.1016/j.gie.2020.06.040.; Hamamoto R., Suvarna K., Yamada M., et al. Application of artificial intelligence technology in oncology: towards the establishment of precision medicine. Cancers. 2020; 12 (12): 3532. https://doi.org/10.3390/cancers12123532.; Bhinder B., Gilvary C., Madhukar N.S., Elemento O. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 2021; 11 (4): 900–15. https://doi.org/10.1158/2159-8290.CD-21-0090.; Ламоткин А.И., Корабельников Д.И., Ламоткин И.А. Искусственный интеллект: основные термины и понятия, применение в здравоохранении и клинической медицине. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2024; 17 (3): 409–15. https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.267.; Siegel R.L., Miller K.D., Fuchs H.E., Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021; 71 (1): 7–33. https://doi.org/10.3322/caac.21654.; McKinney S.M., Sieniek M., Godbole V., et al. International evaluation of an AI system for breast cancer screening. Nature. 2020; 577 (7788): 89–94. https://doi.org/10.1038/s41586-019-1799-6.; Katzman J.L., Shaham U., Cloninger A., et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018; 18 (1): 24. https://doi.org/10.1186/s12874-018-0482-1.; Zadeh Shirazi A., Tofighi M., Gharavi A., Gomez G.A. The application of artificial intelligence to cancer research: a comprehensive guide. Technol Cancer Res Treat. 2024; 23: 15330338241250324. https://doi.org/10.1177/15330338241250324.; Itahashi K., Kondo S., Kubo T., et al. Evaluating clinical genome sequence analysis by Watson for Genomics. Front Med. 2018; 5: 305. https://doi.org/10.3389/fmed.2018.00305.; Guitton T., Allaume P., Rabilloud N., et al. Artificial intelligence in predicting microsatellite instability and KRAS, BRAF mutations from whole-slide images in colorectal cancer: a systematic review. Diagnostics. 2023; 14 (1): 99. https://doi.org/10.3390/diagnostics14010099.; Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25 (1): 44–56. https://doi.org/10.1038/s41591-018-0300-7.; Somashekhar S.P., Sepúlveda M.J., Puglielli S., et al. Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol. 2018; 29 (2): 418–23. https://doi.org/10.1093/annonc/mdx781.; Dacic S., Travis W.D., Giltnane J.M., et al., Artificial intelligence (AI)- powered pathologic response (PathR) assessment of resection specimens after neoadjuvant atezolizumab in patients with non-small cell lung cancer: results from the LCMC3 study. J Clin Oncol. 2021; 39 (15): 106. https://doi.org/10.1200/JCO.2021.39.15_suppl.106.; Beaubier N., Tell R., Lau D., et al. Clinical validation of the tempus xT next-generation targeted oncology sequencing assay. Oncotarget. 2019; 10 (24): 2384–96. https://doi.org/10.18632/oncotarget.26797.; Coombs L., Orlando A., Wang X., et al. A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology. Digit Med. 2022; 5: 117. https://doi.org/10.1038/s41746-022-00660-3.; Bhattacharya S., Saleem S.M., Singh A., et al. Empowering precision medicine: regenerative AI in breast cancer. Front Oncol. 2024; 14: 1465720. https://doi.org/10.3389/fonc.2024.; Kim E.Y., Kim Y.J., Choi W.J., et al. Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: real-world experience with a multicenter health screening cohort. PLoS One. 2022; 17 (2): e0264383. https://doi.org/10.1371/journal.pone.0264383.; Hussain S., Ali M., Naseem U., et al. Breast cancer risk prediction using machine learning: a systematic review. Front Oncol. 2024; 14: 1343627. https://doi.org/10.3389/fonc.2024.; Singh R.S., Masih G.D., Joshi R., et al. Chapter Five – Role of artificial intelligence in cancer diagnostics and therapeutics. In: Sobti R.C., Ganju A.K., Sobti A. (Eds) Biomarkers in cancer detection and monitoring of therapeutics. Academic Press; 2023: 83–97. https://doi.org/10.1016/B978-0-323-95116-6.00015-3.; Ламоткин А.И., Корабельников Д.И., Ламоткин И.А. и др. Искусственный интеллект в здравоохранении и медицине: история ключевых событий, его значимость для врачей, уровень развития в разных странах. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2024; 17 (2): 243–50. https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.254.; Корабельников Д.И., Ламоткин А.И. Эффективность применения искусственного интеллекта в клинической медицине. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2025; 18 (1): 114–24. https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.287.; Storås A.M., Strümke I., Riegler M.A., et al. Artificial intelligence in dry eye disease. Ocul Surf. 2022; 23: 74–86. https://doi.org/10.1016/j.jtos.2021.11.004.; Xu F., Wan C., Zhao L., et al. Predicting post-therapeutic visual acuity and OCT images in patients with central serous chorioretinopathy by artificial intelligence. 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Artif Intell Med. 2021; 113: 102024. https://doi.org/10.1016/j.artmed.2021.102024.; https://www.pharmacoeconomics.ru/jour/article/view/1255

  11. 11
    Academic Journal

    Πηγή: Obstetrics, Gynecology and Reproduction; Online First ; Акушерство, Гинекология и Репродукция; Online First ; 2500-3194 ; 2313-7347

    Περιγραφή αρχείου: application/pdf

    Relation: https://www.gynecology.su/jour/article/view/2521/1363; Даренская А.Д., Румянцев А.А., Гуторов С.Л., Тюляндина А.С. Эволюция системной лекарственной терапии диссеминированного рака эндометрия. Обзор литературы. Злокачественные опухоли. 2023;13(2):80–98. https://doi.org/10.18027/2224-5057-2023-13-2-6.; Кедрова А.Г. Иммунотерапия у больных раком шейки матки. Опухоли женской репродуктивной системы. 2020;16(2):72–7. https://doi.org/10.17650/1994-4098-2020-16-2-72-77.; Ашрафян Л.А., Киселёв В.И., Муйжнек Е.Л. и др. Современные принципы эффективной терапии рака яичников. Опухоли женской репродуктивной системы. 2015;11(2):68–75. https://doi.org/10.17650/1994-4098-2015-11-2-68-75.; Мудунов А.М., Игнатова А.В., Морозова А.С. и др. Комбинированная иммунотаргетная терапия ниволумабом и цетуксимабом: новые возможности в лечении плоскоклеточного рака головы и шеи. Опухоли головы и шеи. 2020;10(3):111–7. https://doi.org/10.17650/2222-1468-2020-10-3-111-117.; Shinde A., Panchal K., Katke S. et al A. Tyrosine kinase inhibitors as next generation oncological therapeutics: current strategies, limitations and future perspectives. Therapie. 2022;77(4):425–43. https://doi.org/10.1016/j.therap.2021.10.010.; Schneider B.J., Naidoo J., Santomasso B.D. et al. Management of immune-related adverse events in patients treated with immune checkpoint inhibitor therapy: ASCO Guideline Update. J Clin Oncol. 2021;39(36):4073–126. https://doi.org/10.1200/JCO.21.01440.; Thompson J.A., Schneider B.J,. Brahmer J. et al. Management of Immunotherapy-Related Toxicities, Version 1.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2022;20(4):387–405. https://doi.org/10.6004/jnccn.2022.0020.; Liu X., Wang Z., Zhao C. et al. 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  12. 12
    Academic Journal

    Πηγή: Russian Journal of Pediatric Hematology and Oncology; Том 12, № 1 (2025); 48-54 ; Российский журнал детской гематологии и онкологии (РЖДГиО); Том 12, № 1 (2025); 48-54 ; 2413-5496 ; 2311-1267

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    Academic Journal

    Πηγή: Medical Visualization; Принято в печать ; Медицинская визуализация; Принято в печать ; 2408-9516 ; 1607-0763

    Περιγραφή αρχείου: application/pdf

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    Συνεισφορές: The study was conducted without sponsorship, Исследование проведено без спонсорской поддержки

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    Πηγή: Annals of the Russian academy of medical sciences; Vol 79, No 6 (2024); 490-506 ; Вестник Российской академии медицинских наук; Vol 79, No 6 (2024); 490-506 ; 2414-3545 ; 0869-6047 ; 10.15690/vramn.796

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    Συνεισφορές: 0

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