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Artykuły w czasopismach na temat "Multi-Organ"
Dickerson, John P., i Tuomas Sandholm. "Multi-Organ Exchange". Journal of Artificial Intelligence Research 60 (26.11.2017): 639–79. http://dx.doi.org/10.1613/jair.4919.
Pełny tekst źródłaDickerson, J., i T. Sandholm. "Toward Multi-Organ Exchange." Transplantation 98 (lipiec 2014): 811–12. http://dx.doi.org/10.1097/00007890-201407151-02768.
Pełny tekst źródłaWiberg, Candice A., Barbara F. Prusak, Raymond Pollak i Martin F. Mozes. "Multi-Organ Donor Procurement". AORN Journal 44, nr 6 (grudzień 1986): 936–43. http://dx.doi.org/10.1016/s0001-2092(07)65477-5.
Pełny tekst źródłaBibas, Benoit Jacques, Angelo Fernandez, Paulo Manuel Pêgo-Fernandes i Fabio Biscegli Jatene. "Disseminated multi-organ tuberculosis". European Journal of Cardio-Thoracic Surgery 39, nr 6 (czerwiec 2011): 1080. http://dx.doi.org/10.1016/j.ejcts.2011.01.049.
Pełny tekst źródłaTuttle-Newhall, Janet E., Bradley H. Collins, Paul C. Kuo i Rebecca Schoeder. "Organ donation and treatment of the multi-organ donor". Current Problems in Surgery 40, nr 5 (maj 2003): 266–310. http://dx.doi.org/10.1067/msg.2003.120005.
Pełny tekst źródłaBisigniano, Liliana, Viviana V. Tagliafichi, Daniela D. Hansen Krogh, Paula P. Furman i Maria del Carmen Bacque. "Kidney Transplant With Multi-Organ and Mono-Organ Donor". Transplantation 101 (sierpień 2017): S91. http://dx.doi.org/10.1097/01.tp.0000525121.72116.0b.
Pełny tekst źródłaPatra, Manas, Ebibeni Ngullie i Debojyoti Borkotoky. "Multi-Organ Anomalies in Pig". International Journal of Livestock Research 4, nr 1 (2014): 143. http://dx.doi.org/10.5455/ijlr.20131002072225.
Pełny tekst źródłaHabor, Adriana, Noémi Ecaterina Sidlovszky, Edina Török, Iunius Simu, Monica Copotoiu i Larisa Mureșan. "Recurrent Multi-Organ Cystic Echinococcosis". Internal Medicine 15, nr 2 (1.05.2018): 59–67. http://dx.doi.org/10.2478/inmed-2018-0015.
Pełny tekst źródłaLee, Seung, i Jong Sung. "Microtechnology-Based Multi-Organ Models". Bioengineering 4, nr 4 (21.05.2017): 46. http://dx.doi.org/10.3390/bioengineering4020046.
Pełny tekst źródłaHarjes, Ulrike. "Mimicking multi-organ drug effects". Nature Reviews Cancer 19, nr 8 (9.07.2019): 417. http://dx.doi.org/10.1038/s41568-019-0175-z.
Pełny tekst źródłaRozprawy doktorskie na temat "Multi-Organ"
Mali, Shruti Atul. "Multi-Modal Learning for Abdominal Organ Segmentation". Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285866.
Pełny tekst źródłaKarlsson, Albin, i Daniel Olmo. "Multi-organ segmentation med användning av djup inlärning". Thesis, KTH, Medicinteknik och hälsosystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277813.
Pełny tekst źródłaMedical image analysis is both time consuming and requires expertise. In thisreport, a 2.5D version of the U-net convolution network adapted for automatedkidney segmentation is further developed. Convolution neural networks havepreviously shown expert level performance in image segmentation. Training datafor the network was created by manually segmenting MRI images of kidneys.The 2.5D U-Net network was trained with 64 kidney segmentations fromprevious work. Volume analysis on the network’s kidney segmentation proposalsof 38,000 patients showed that the ammount of segmented voxels that are notpart of the kidneys was 0.35%. After the addition of 56 of our segmentations, itdecreased to just 0.11%, indicating a reduction of about 68%. This is a majorimprovement of the network and an important step towards the development ofpractical applications of automated segmentation.
Carrizo, Gabriel. "Organ Segmentation Using Deep Multi-task Learning with Anatomical Landmarks". Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241640.
Pełny tekst źródłaJacobzon, Gustaf. "Multi-site Organ Detection in CT Images using Deep Learning". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279290.
Pełny tekst źródłaVid optimering av en kontrollerad dos inom strålbehandling krävs det information om friska organ, så kallade riskorgan, i närheten av de maligna cellerna för att minimera strålningen i dessa organ. Denna information kan tillhandahållas av djupa volymetriskta segmenteringsnätverk, till exempel 3D U-Net. Begränsningar i minnesstorleken hos moderna grafikkort gör att det inte är möjligt att träna ett volymetriskt segmenteringsnätverk på hela bildvolymen utan att först nedsampla volymen. Detta leder dock till en lågupplöst segmentering av organen som inte är tillräckligt precis för att kunna användas vid optimeringen. Ett alternativ är att endast behandla en intresseregion som innesluter ett eller ett fåtal organ från bildvolymen och träna ett regionspecifikt nätverk på denna mindre volym. Detta tillvägagångssätt kräver dock information om vilket område i bildvolymen som ska skickas till det regionspecifika segmenteringsnätverket. Denna information kan tillhandahållas av ett 3Dobjektdetekteringsnätverk. I regel är även detta nätverk regionsspecifikt, till exempel thorax-regionen, och kräver mänsklig assistans för att välja rätt nätverk för en viss region i kroppen. Vi föreslår istället ett multiregions-detekteringsnätverk baserat påYOLOv3 som kan detektera 43 olika organ och fungerar på godtyckligt valda axiella fönster i kroppen. Vår modell identifierar närvarande organ (hela eller trunkerade) i bilden och kan automatiskt ge information om vilken region som ska behandlas av varje regionsspecifikt segmenteringsnätverk. Vi tränar vår modell på fyra små (så lågt som 20 bilder) platsspecifika datamängder med svag övervakning för att hantera den delvis icke-annoterade egenskapen hos datamängderna. Vår modell genererar en organ-specifik intresseregion för 92 % av organen som finns i testmängden.
Hasal, Steven John 1965. "A model for the multi-organ metabolism and nephrotoxicity of chlorotrifluoroethylene". Diss., The University of Arizona, 1998. http://hdl.handle.net/10150/288818.
Pełny tekst źródłaBoussaid, Haithem. "Efficient inference and learning in graphical models for multi-organ shape segmentation". Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0002/document.
Pełny tekst źródłaThis thesis explores the use of discriminatively trained deformable contour models (DCMs) for shape-based segmentation in medical images. We make contributions in two fronts: in the learning problem, where the model is trained from a set of annotated images, and in the inference problem, whose aim is to segment an image given a model. We demonstrate the merit of our techniques in a large X-Ray image segmentation benchmark, where we obtain systematic improvements in accuracy and speedups over the current state-of-the-art. For learning, we formulate training the DCM scoring function as large-margin structured prediction and construct a training objective that aims at giving the highest score to the ground-truth contour configuration. We incorporate a loss function adapted to DCM-based structured prediction. In particular, we consider training with the Mean Contour Distance (MCD) performance measure. Using this loss function during training amounts to scoring each candidate contour according to its Mean Contour Distance to the ground truth configuration. Training DCMs using structured prediction with the standard zero-one loss already outperforms the current state-of-the-art method [Seghers et al. 2007] on the considered medical benchmark [Shiraishi et al. 2000, van Ginneken et al. 2006]. We demonstrate that training with the MCD structured loss further improves over the generic zero-one loss results by a statistically significant amount. For inference, we propose efficient solvers adapted to combinatorial problems with discretized spatial variables. Our contributions are three-fold:first, we consider inference for loopy graphical models, making no assumption about the underlying graph topology. We use an efficient decomposition-coordination algorithm to solve the resulting optimization problem: we decompose the model’s graph into a set of open, chain-structured graphs. We employ the Alternating Direction Method of Multipliers (ADMM) to fix the potential inconsistencies of the individual solutions. Even-though ADMMis an approximate inference scheme, we show empirically that our implementation delivers the exact solution for the considered examples. Second,we accelerate optimization of chain-structured graphical models by using the Hierarchical A∗ search algorithm of [Felzenszwalb & Mcallester 2007] couple dwith the pruning techniques developed in [Kokkinos 2011a]. We achieve a one order of magnitude speedup in average over the state-of-the-art technique based on Dynamic Programming (DP) coupled with Generalized DistanceTransforms (GDTs) [Felzenszwalb & Huttenlocher 2004]. Third, we incorporate the Hierarchical A∗ algorithm in the ADMM scheme to guarantee an efficient optimization of the underlying chain structured subproblems. The resulting algorithm is naturally adapted to solve the loss-augmented inference problem in structured prediction learning, and hence is used during training and inference. In Appendix A, we consider the case of 3D data and we develop an efficientmethod to find the mode of a 3D kernel density distribution. Our algorithm has guaranteed convergence to the global optimum, and scales logarithmically in the volume size by virtue of recursively subdividing the search space. We use this method to rapidly initialize 3D brain tumor segmentation where we demonstrate substantial acceleration with respect to a standard mean-shift implementation. In Appendix B, we describe in more details our extension of the Hierarchical A∗ search algorithm of [Felzenszwalb & Mcallester 2007] to inference on chain-structured graphs
Allen, Elizabeth Jane. "Multi-organ rheumatological disease : statistical analysis of outcome measures and their interrelationships". Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/1446556/.
Pełny tekst źródłaMillar, Benjamin John Minford. "The role of the formyl-peptide receptor in multi-organ fibrosis mechanisms". Thesis, University of Newcastle upon Tyne, 2016. http://hdl.handle.net/10443/3500.
Pełny tekst źródłaSamarakoon, Prasad. "Random Regression Forests for Fully Automatic Multi-Organ Localization in CT Images". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM039/document.
Pełny tekst źródłaLocating an organ in a medical image by bounding that particular organ with respect to an entity such as a bounding box or sphere is termed organ localization. Multi-organ localization takes place when multiple organs are localized simultaneously. Organ localization is one of the most crucial steps that is involved in all the phases of patient treatment starting from the diagnosis phase to the final follow-up phase. The use of the supervised machine learning technique called random forests has shown very encouraging results in many sub-disciplines of medical image analysis. Similarly, Random Regression Forests (RRF), a specialization of random forests for regression, have produced the state of the art results for fully automatic multi-organ localization.Although, RRF have produced state of the art results in multi-organ segmentation, the relative novelty of the method in this field still raises numerous questions about how to optimize its parameters for consistent and efficient usage. The first objective of this thesis is to acquire a thorough knowledge of the inner workings of RRF. After achieving the above mentioned goal, we proposed a consistent and automatic parametrization of RRF. Then, we empirically proved the spatial indenpendency hypothesis used by RRF. Finally, we proposed a novel RRF specialization called Light Random Regression Forests for multi-organ localization
Olde, Damink Stephanus Wilibrordus Maria Jalan Rajiv. "Pathophysiological basis of hepatic encephalopathy: a multi-organ perspective in patients with liver failure". Maastricht : Maastricht : Universiteit Maastricht ; University Library, Maastricht University [Host], 2005. http://arno.unimaas.nl/show.cgi?fid=6361.
Pełny tekst źródłaKsiążki na temat "Multi-Organ"
D, Higgins Robert S., red. The multi-organ donor: Selection and management. Abingdon, Oxon, England: Blackwell Science, 1997.
Znajdź pełny tekst źródłaCanadian Symposium on Multi-Organ Transplantation (1st 1988 University Hospital, London, Ont.). First Canadian symposium on multi-organ transplantation. Redaktorzy Grant David R, Wall William J, Kucherawy Mary Ann, University of Western Ontario, University Hospital (London, Ont.) i Canadian Symposium on Multi-Organ Transplantation. London, Ont: SCITEX Publications, 1989.
Znajdź pełny tekst źródłaSzema, Anthony M., red. World Trade Center Pulmonary Diseases and Multi-Organ System Manifestations. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-59372-2.
Pełny tekst źródłaLeeds Teaching Hospitals NHS Trust., Bradford Hospitals NHS Trust. Renal Unit. i Great Britain. Department of Health., red. Organ donation & transplantation: The multi-faith perspective : National Museum of Photography, Film and Television, Bradford, March 20 2000. Bradford: Renal Unit at the Bradford Hospitals NHS Trust, 2000.
Znajdź pełny tekst źródłaDavis, Stephanie Duggins, Margaret Rosenfeld i James Chmiel. Cystic Fibrosis: A Multi-Organ System Approach. Springer International Publishing AG, 2021.
Znajdź pełny tekst źródłaDavis, Stephanie Duggins, Margaret Rosenfeld i James Chmiel. Cystic Fibrosis: A Multi-Organ System Approach. Humana, 2020.
Znajdź pełny tekst źródłaBaldwin, John, Juan Sanchez i Marc Lorber. The Multi-Organ Donor: Selection and Management. Blackwell Science, Ltd. (UK), 1997.
Znajdź pełny tekst źródłaMitchell P., M.D. Fink. The Pathophysiology of Sepsis and Multi-Organ Failure. Chapman & Hall, 1997.
Znajdź pełny tekst źródłaWorld Trade Center Pulmonary Diseases and Multi-Organ System Manifestations. Springer, 2017.
Znajdź pełny tekst źródłaSzema, Anthony M. World Trade Center Pulmonary Diseases and Multi-Organ System Manifestations. Springer, 2018.
Znajdź pełny tekst źródłaCzęści książek na temat "Multi-Organ"
Nasreen, Fahtiha, Tasnia Noor Salim, Avishek Kunda, Ibad Ur Rehman, Maryam Aman i Rebecca Caruana. "Multi-organ Transplantation". W Heart Transplantation, 231–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17311-0_13.
Pełny tekst źródłaMarik, Paul Ellis. "Multi-organ Dysfunction Syndrome". W Handbook of Evidence-Based Critical Care, 593–97. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5923-2_57.
Pełny tekst źródłaKlingemann, H. G. "Single and Multi-Organ Failure". W A Guide to Blood and Marrow Transplantation, 159–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-18248-8_16.
Pełny tekst źródłaDawahra, M., X. Martin, P. Cloix, L. Tajra i J. M. Dubernard. "Surgical optimization of multi-organ procurement". W Organ Shortage: The Solutions, 197–205. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0201-8_26.
Pełny tekst źródłaChen, Zhonghua, Tapani Ristaniemi, Fengyu Cong i Hongkai Wang. "Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling". W Advances in Neural Networks – ISNN 2020, 74–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64221-1_7.
Pełny tekst źródłaKlimas, Natasha, Josephine Quintanilla-Dieck i Travis Vandergriff. "Drug-Induced Delayed Multi-organ Hypersensitivity Syndrome". W Cutaneous Drug Eruptions, 271–79. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6729-7_25.
Pełny tekst źródłaDruml, W. "Multi-Organ-Versagen: das Versagen der Zelle". W Multiorganversagen, 1–8. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-9201-6_1.
Pełny tekst źródłaCassimjee, Sarah. "Critical Care, Respiratory and Multi-Organ Failure". W Nutrition and HIV, 427–41. West Sussex, UK: John Wiley & Sons Ltd., 2013. http://dx.doi.org/10.1002/9781118786529.ch21.
Pełny tekst źródłaSong, Haobo, Chang Liu, Lukas Folle i Andreas Maier. "Multi-organ Segmentation with Partially Annotated Datasets". W Informatik aktuell, 216–21. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36932-3_46.
Pełny tekst źródłaOkada, Toshiyuki, Keita Yokota, Masatoshi Hori, Masahiko Nakamoto, Hironobu Nakamura i Yoshinobu Sato. "Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images". W Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 502–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_60.
Pełny tekst źródłaStreszczenia konferencji na temat "Multi-Organ"
Goëau, Hervé, Pierre Bonnet, Julien Barbe, Vera Bakic, Alexis Joly, Jean-François Molino, Daniel Barthelemy i Nozha Boujemaa. "Multi-organ plant identification". W the 1st ACM international workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2390832.2390843.
Pełny tekst źródłaLee, Sue Han, Yang Loong Chang, Chee Seng Chan i Paolo Remagnino. "HGO-CNN: Hybrid generic-organ convolutional neural network for multi-organ plant classification". W 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8297126.
Pełny tekst źródłaOkada, T., M. G. Linguraru, M. Hori, Y. Suzuki, R. M. Summers, N. Tomiyama i Y. Sato. "Multi-organ segmentation in abdominal CT images". W 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346840.
Pełny tekst źródłaCantemir-Stone, Carmen Z., Nathan Marzlin, Jie Zhang, Kevin Wenzke, Kun Huang i Clay B. Marsh. "Unique Gene Signature For Multi-Organ Fibrosis". W American Thoracic Society 2012 International Conference, May 18-23, 2012 • San Francisco, California. American Thoracic Society, 2012. http://dx.doi.org/10.1164/ajrccm-conference.2012.185.1_meetingabstracts.a4934.
Pełny tekst źródłaAli, Asfand Yar, i Labiba Gillani Fahad. "Multi-Organ Plant Classification Using Deep Learning". W 2022 24th International Multitopic Conference (INMIC). IEEE, 2022. http://dx.doi.org/10.1109/inmic56986.2022.9972979.
Pełny tekst źródłaHu, JiaWen, i Kai Wang. "Abdominal Multi-Organ Segmentation Based on nnUNet". W 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2023. http://dx.doi.org/10.1109/icsp58490.2023.10248829.
Pełny tekst źródłaZhang, Lefei, Shixiang Feng, Yu Wang, Yanfeng Wang, Ya Zhang, Xin Chen i Qi Tian. "Unsupervised Ensemble Distillation for Multi-Organ Segmentation". W 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761568.
Pełny tekst źródłaZhang, Lefei, Shixiang Feng, Yu Wang, Yanfeng Wang, Ya Zhang, Xin Chen i Qi Tian. "Unsupervised Ensemble Distillation for Multi-Organ Segmentation". W 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761568.
Pełny tekst źródłaJoyseeree, Ranveer R., i Henning Müller. "Locating seed points for automatic multi-organ segmentation using non-rigid registration and organ annotations". W SPIE Medical Imaging, redaktorzy Sébastien Ourselin i Martin A. Styner. SPIE, 2015. http://dx.doi.org/10.1117/12.2081204.
Pełny tekst źródłaBrosch, Tom, i Axel Saalbach. "Foveal fully convolutional nets for multi-organ segmentation". W Image Processing, redaktorzy Elsa D. Angelini i Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293528.
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