Готові списки джерел за темами / Multi-Organ

Добірка наукової літератури з теми "Multi-Organ"

Автор: Grafiati
Опубліковано: 8 лютого 2024

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Статті в журналах з теми "Multi-Organ"

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Dickerson, John P., and Tuomas Sandholm. "Multi-Organ Exchange." Journal of Artificial Intelligence Research 60 (November 26, 2017): 639–79. http://dx.doi.org/10.1613/jair.4919.

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Kidney exchange, where candidates with organ failure trade incompatible but willing donors, is a life-saving alternative to the deceased donor waitlist, which has inadequate supply to meet demand. While fielded kidney exchanges see huge benefit from altruistic kidney donors (who give an organ without a paired needy candidate), a significantly higher medical risk to the donor deters similar altruism with livers. In this paper, we begin by exploring the idea of large-scale liver exchange, and show on demographically accurate data that vetted kidney exchange algorithms can be adapted to clear such an exchange at the nationwide level. We then propose cross-organ donation where kidneys and livers can be bartered for each other. We show theoretically that this multi-organ exchange provides linearly more transplants than running separate kidney and liver exchanges. This linear gain is a product of altruistic kidney donors creating chains that thread through the liver pool; it exists even when only a small but constant portion of the donors on the kidney side of the pool are willing to donate a liver lobe. We support this result experimentally on demographically accurate multi-organ exchanges. We conclude with thoughts regarding the fielding of a nationwide liver or joint liver-kidney exchange from a legal and computational point of view.
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Dickerson, J., and T. Sandholm. "Toward Multi-Organ Exchange." Transplantation 98 (July 2014): 811–12. http://dx.doi.org/10.1097/00007890-201407151-02768.

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3

Wiberg, Candice A., Barbara F. Prusak, Raymond Pollak, and Martin F. Mozes. "Multi-Organ Donor Procurement." AORN Journal 44, no. 6 (December 1986): 936–43. http://dx.doi.org/10.1016/s0001-2092(07)65477-5.

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Bibas, Benoit Jacques, Angelo Fernandez, Paulo Manuel Pêgo-Fernandes, and Fabio Biscegli Jatene. "Disseminated multi-organ tuberculosis." European Journal of Cardio-Thoracic Surgery 39, no. 6 (June 2011): 1080. http://dx.doi.org/10.1016/j.ejcts.2011.01.049.

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Tuttle-Newhall, Janet E., Bradley H. Collins, Paul C. Kuo, and Rebecca Schoeder. "Organ donation and treatment of the multi-organ donor." Current Problems in Surgery 40, no. 5 (May 2003): 266–310. http://dx.doi.org/10.1067/msg.2003.120005.

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Bisigniano, Liliana, Viviana V. Tagliafichi, Daniela D. Hansen Krogh, Paula P. Furman, and Maria del Carmen Bacque. "Kidney Transplant With Multi-Organ and Mono-Organ Donor." Transplantation 101 (August 2017): S91. http://dx.doi.org/10.1097/01.tp.0000525121.72116.0b.

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Patra, Manas, Ebibeni Ngullie, and Debojyoti Borkotoky. "Multi-Organ Anomalies in Pig." International Journal of Livestock Research 4, no. 1 (2014): 143. http://dx.doi.org/10.5455/ijlr.20131002072225.

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Habor, Adriana, Noémi Ecaterina Sidlovszky, Edina Török, Iunius Simu, Monica Copotoiu, and Larisa Mureșan. "Recurrent Multi-Organ Cystic Echinococcosis." Internal Medicine 15, no. 2 (May 1, 2018): 59–67. http://dx.doi.org/10.2478/inmed-2018-0015.

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AbstractCystic echinococcosis is the disease that occupies, together with trichinosis, the first place in the zoonoses in Romania. We present the case of a 75-year-old urban patient known for echinococcosis, firstly operated for bone cysts in the right coxofemoral joint at the age of 24, then in 2000 she was operated for a lung hydatid cyst and in 2011 she underwent a surgery for recurrent bone echinococcosis.After a 7-year lull, she returns due to the appearance of tumorous masses in the abdominal right flank, the right thigh, accompanied by pain in the right coxofemoral joint, functional impotence of the right lower limb, asthenia, anorexia. Based on clinical, immunological, imagistic examinations, the diagnosis of cystic echinococcosis localised in the liver, bone and muscle was established. Since the patient in association had ischaemic heart disease in NYHA III (New York Heart Association) congestive heart failure, surgical treatment was delayed and preoperative treatment with Albendazole 10-15mg/kg/day was started. Initially we will apply a conservative treatment, laparoscopic drainage and aspiration of the contents, saline instillation and aspiration.
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Lee, Seung, and Jong Sung. "Microtechnology-Based Multi-Organ Models." Bioengineering 4, no. 4 (May 21, 2017): 46. http://dx.doi.org/10.3390/bioengineering4020046.

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Harjes, Ulrike. "Mimicking multi-organ drug effects." Nature Reviews Cancer 19, no. 8 (July 9, 2019): 417. http://dx.doi.org/10.1038/s41568-019-0175-z.

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Більше джерел

Дисертації з теми "Multi-Organ"

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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.

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Анотація:
Deep Learning techniques are widely used across various medical imaging applications. However, they are often fine-tuned for a specific modality and are not generalizable when it comes to new modalities or datasets. One of the main reasons for this is large data variations for e.g., the dynamic range of intensity values is large across multi-modal images. The goal of the project is to develop a method to address multi-modal learning that aims at segmenting liver from Computed Tomography (CT) images and abdominal organs from Magnetic Resonance (MR) images using deep learning techniques. In this project, a self-supervised approach is adapted to attain domain adaptation across images while retaining important 3D information from medical images using a simple 3D-UNet with a few auxiliary tasks. The method comprises of two main steps: representation learning via self-supervised learning (pre-training) and fully supervised learning (fine-tuning). Pre-training is done using a 3D-UNet as a base model along with some auxiliary data augmentation tasks to learn representation through texture, geometry and appearances. The second step is fine-tuning the same network, without the auxiliary tasks, to perform the segmentation tasks on CT and MR images. The annotations of all organs are not available in both modalities. Thus the first step is used to learn general representation from both image modalities; while the second step helps to fine-tune the representations to the available annotations of each modality. Results obtained for each modality were submitted online, and one of the evaluations obtained was in the form of DICE score. The results acquired showed that the highest DICE score of 0.966 was obtained for CT liver prediction and highest DICE score of 0.7 for MRI abdominal segmentation. This project shows the potential to achieve desired results by combining both self and fully-supervised approaches.
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Karlsson, Albin, and 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.

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Анотація:
Medicinsk bildanalys är både tidskonsumerade och kräver expertis. I den härrapporten vidareutvecklas en 2.5D version av faltningsnätverket U-Net anpassadför automatiserad njuresegmentering. Faltningsnätverk har tidigare visatliknande prestation som experter. Träningsdata för nätverket anpassades genomatt manuellt segmentera MR-bilder av njurar. 2.5D U-Net nätverket tränades med64 st njursegmenteringar från tidigare arbete. Volymanalys på nätverketssegmenterings förslag av 38.000 patienter visade den mängden segmenteradevoxlar som inte tillhörde njurarna var 0,35 %. Efter tillägg av 56 st av vårasegmenteringar minskade det till 0.11 %, en reduktion av cirka 68 %. Det är enstor förbättring av nätverket och ett viktigt steg mot tillämpning avautomatiserad segmentering.
Medical 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.
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3

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.

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Анотація:
This master thesis is the study of multi-task learning to train a neural network to segment medical images and predict anatomical landmarks. The paper shows the results from experiments using medical landmarks in order to attempt to help the network learn the important organ structures quicker. The results found in this study are inconclusive and rather than showing the efficiency of the multi-task framework for learning, they tell a story of the importance of choosing the tasks and dataset wisely. The study also reflects and depicts the general difficulties and pitfalls of performing a project of this type.
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4

Jacobzon, 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.

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Анотація:
When optimizing a controlled dose in radiotherapy, high resolution spatial information about healthy organs in close proximity to the malignant cells are necessary in order to mitigate dispersion into these organs-at-risk. This information can be provided by deep volumetric segmentation networks, such as 3D U-Net. However, due to limitations of memory in modern graphical processing units, it is not feasible to train a volumetric segmentation network on full image volumes and subsampling the volume gives a too coarse segmentation. An alternative is to sample a region of interest from the image volume and train an organ-specific network. This approach requires knowledge of which region in the image volume that should be sampled and can be provided by a 3D object detection network. Typically the detection network will also be region specific, although a larger region such as the thorax region, and requires human assistance in choosing the appropriate network for a certain region in the body.  Instead, we propose a multi-site object detection network based onYOLOv3 trained on 43 different organs, which may operate on arbitrary chosen axial patches in the body. Our model identifies the organs present (whole or truncated) in the image volume and may automatically sample a region from the input and feed to the appropriate volumetric segmentation network. We train our model on four small (as low as 20 images) site-specific datasets in a weakly-supervised manner in order to handle the partially unlabeled nature of site-specific datasets. Our model is able to generate organ-specific regions of interests that enclose 92% of the organs present in the test set.
Vid 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.
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5

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.

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During the past decade precision-cut tissue slices have begun to be utilized for toxicity and metabolism studies. These studies have primarily involved a single organ type. In this study, a new preparation of rat renal cortical slices was validated and used to investigate the toxicity of chlorotrifluoroethylene and its cysteine and glutathione conjugates. An additional level of complexity was added by utilizing a sequential incubation system in which rat renal cortical slices were directly incubated in the medium from liver slice incubations. Once the new renal slice preparation and sequential incubation system had been validated, these new methods were used to study the mechanism of toxicity of chlorotrifluoroethylene and it metabolites. The hypothesis being tested in these studies is that sequential biotransformation in the liver and the kidney is required for CTFE nephrotoxicity. In these studies I developed a sequential incubation system with precision-cut rat liver slices as the drug activating system and renal cortical slices as the target tissue for toxicity. Utilizing the sequential incubation system, I found that first incubating liver slices with CTFE and then transferring kidney slices to this liver slice incubation medium causes toxicity in the kidney slices. I also found that this toxicity correlates well with the toxicity observed with kidney slice incubations with the cysteine and glutathione conjugates of CTFE. By incubating slices with inhibitors of the various enzymes in the proposed metabolic pathway of CTFE, it was determined that glutathione conjugation in the liver and subsequent degradation by gamma-glutamyltranspeptidase are important steps in toxicity of CTFE. Although previous research with inhibitors of β-lyase have indicated that β-lyase is an essential enzyme in the bioactivation of CTFE, inhibition of the pyridoxal phosphate cofactor of this enzyme in renal slices did not reduce the toxicity of conjugates of CTFE. There was no reduction in toxicity when dipeptidases were inhibited when transport via the organic anion transporter or neutral amino acid transporter were inhibited. These data indicate that the glutathione conjugate of CTFE is formed in the liver and that the subsequent metabolism of this glutathione conjugate in the kidney is required for nephrotoxicity.
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6

Boussaid, 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.

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Анотація:
Cette thèse explore l’utilisation des modèles de contours déformables pour la segmentation basée sur la forme des images médicales. Nous apportons des contributions sur deux fronts: dans le problème de l’apprentissage statistique, où le modèle est formé à partir d’un ensemble d’images annotées, et le problème de l’inférence, dont le but est de segmenter une image étant donnée un modèle. Nous démontrons le mérite de nos techniques sur une grande base d’images à rayons X, où nous obtenons des améliorations systématiques et des accélérations par rapport à la méthode de l’état de l’art. Concernant l’apprentissage, nous formulons la formation de la fonction de score des modèles de contours déformables en un problème de prédiction structurée à grande marge et construisons une fonction d’apprentissage qui vise à donner le plus haut score à la configuration vérité-terrain. Nous intégrons une fonction de perte adaptée à la prédiction structurée pour les modèles de contours déformables. En particulier, nous considérons l’apprentissage avec la mesure de performance consistant en la distance moyenne entre contours, comme une fonction de perte. L’utilisation de cette fonction de perte au cours de l’apprentissage revient à classer chaque contour candidat selon sa distance moyenne du contour vérité-terrain. Notre apprentissage des modèles de contours déformables en utilisant la prédiction structurée avec la fonction zéro-un de perte surpasse la méthode [Seghers et al. 2007] de référence sur la base d’images médicales considérée [Shiraishi et al. 2000, van Ginneken et al. 2006]. Nous démontrons que l’apprentissage avec la fonction de perte de distance moyenne entre contours améliore encore plus les résultats produits avec l’apprentissage utilisant la fonction zéro-un de perte et ce d’une quantité statistiquement significative.Concernant l’inférence, nous proposons des solveurs efficaces et adaptés aux problèmes combinatoires à variables spatiales discrétisées. Nos contributions sont triples: d’abord, nous considérons le problème d’inférence pour des modèles graphiques qui contiennent des boucles, ne faisant aucune hypothèse sur la topologie du graphe sous-jacent. Nous utilisons un algorithme de décomposition-coordination efficace pour résoudre le problème d’optimisation résultant: nous décomposons le graphe du modèle en un ensemble de sous-graphes en forme de chaines ouvertes. Nous employons la Méthode de direction alternée des multiplicateurs (ADMM) pour réparer les incohérences des solutions individuelles. Même si ADMM est une méthode d’inférence approximative, nous montrons empiriquement que notre implémentation fournit une solution exacte pour les exemples considérés. Deuxièmement, nous accélérons l’optimisation des modèles graphiques en forme de chaîne en utilisant l’algorithme de recherche hiérarchique A* [Felzenszwalb & Mcallester 2007] couplé avec les techniques d’élagage développés dans [Kokkinos 2011a]. Nous réalisons une accélération de 10 fois en moyenne par rapport à l’état de l’art qui est basé sur la programmation dynamique (DP) couplé avec les transformées de distances généralisées [Felzenszwalb & Huttenlocher 2004]. Troisièmement, nous intégrons A* dans le schéma d’ADMM pour garantir une optimisation efficace des sous-problèmes en forme de chaine. En outre, l’algorithme résultant est adapté pour résoudre les problèmes d’inférence augmentée par une fonction de perte qui se pose lors de l’apprentissage de prédiction des structure, et est donc utilisé lors de l’apprentissage et de l’inférence. [...]
This 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
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7

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/.

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Анотація:
Idiopathic inflammatory myopathies are usually regarded as a heterogeneous group of autoimmune rheumatic diseases. Dermatomyositis and polymyositis may affect children and adults and, although rare, are a major cause of disability. In order to assess the value of conventional and newer therapies, a core set of measures for assessing myositis outcomes are being developed . This thesis reports on the design and analysis of two real patient exercises carried out to study proposed measures. An approach to the study of reliability and agreement is presented. Inference procedures for ratios of standard errors are developed. The myostitis measures are based on previous work in systemic lupus erythematosus (lupus), a major autoimmune rheumatic disease. International attempts to define validated disease activity and damage indices to assess patients with lupus have provided a consistent way to assess the disease. However, its multiple clinical manifestations prove a great challenge to rheumatologists managing patients with lupus. There is a need to better understand predictors of disease activity in order to improve and standardize therapy and to prevent the development of chronic damage. This thesis presents an analysis of a clinical database for patients with lupus. The aim is to develop approaches to examine the interrelationships between disease activity in the different organ systems. The database available for analysis consists of data collected on 440 patients over a period of 10 years. The analysis is based on logistic regression methodology with outcomes defined at the times of clinic visits. The usefulness of separate logistic regressions with dynamic covariates for the analysis of multinomial panel data is illustrated. The efficiency of the approach relative to modelling disease activity in continuous time is investigated.
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8

Millar, 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.

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Анотація:
Mitochondrial Damage-associated molecular patterns (mtDAMPs) are an emerging source of endogenous alarmins. N-formylated peptides bind members of the formyl-peptide receptor (FPR) family. From its original role in chemotaxis of immune cells towards sites of infection the part that this G-protein coupled receptor (GPCR) plays in the human body is expanding with expression evident in cells of non-phagocyte origin as well as neutrophils and macrophage. To investigate how FPR1 affects the development of pulmonary fibrosis the bleomycin acute injury in vivo model was employed as its pathogenesis shares features with Idiopathic pulmonary fibrosis (IPF). Transgenic mice lacking functional fpr1 displayed a reduced inflammatory profile and fibrotic phenotype at acute and end-stage endpoints respectively post-bleomycin instillation. In vivo models of fibrosis in different organs such as the liver and kidney there was not the same protective effect with deletion of fpr1 as with acute bleomycin lung injury mechanism. This in turn brought the pathogenesis of the in vivo models into question particularly due to the abundance of fpr1 expression on neutrophils, the first line of defense of the immune system. By depleting neutrophils prior to the bleomycin injury the nature of these myeloid cells in this lung fibrosis model and through evaluation of the inflammatory and fibrotic phases post-instillation it is evident that these cells play a major role in how the disease develops. Translation to the human disease (IPF) was a vital step to elucidate the true role of FPR1 in chronic fibrosis mechanisms. Expression was demonstrated by immunofluorescence in CD45+ leukocytes as well as in isolated fibroblasts. This was corroborated by mRNA levels in primary cultured cells when FPR1 expression was ‘primed’ by inflammatory stimuli such as lipopolysaccharide (LPS). With effects observed in a murine setting and also in primary tissue/cells the FPR1 effect may be microenvironment/neutrophil dependent.
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9

Samarakoon, 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.

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Анотація:
La localisation d'un organe dans une image médicale en délimitant cet organe spécifique par rapport à une entité telle qu'une boite ou sphère englobante est appelée localisation d'organes. La localisation multi-organes a lieu lorsque plusieurs organes sont localisés simultanément. La localisation d'organes est l'une des étapes les plus cruciales qui est impliquée dans toutes les phases du traitement du patient à partir de la phase de diagnostic à la phase finale de suivi. L'utilisation de la technique d'apprentissage supervisé appelée forêts aléatoires (Random Forests) a montré des résultats très encourageants dans de nombreuses sous-disciplines de l'analyse d'images médicales. De même, Random Regression Forests (RRF), une spécialisation des forêts aléatoires pour la régression, ont produit des résultats de l'état de l'art pour la localisation automatique multi-organes.Bien que l'état de l'art des RRF montrent des résultats dans la localisation automatique de plusieurs organes, la nouveauté relative de cette méthode dans ce domaine soulève encore de nombreuses questions sur la façon d'optimiser ses paramètres pour une utilisation cohérente et efficace. Basé sur une connaissance approfondie des rouages des RRF, le premier objectif de cette thèse est de proposer une paramétrisation cohérente et automatique des RRF. Dans un second temps, nous étudions empiriquement l'hypothèse d'indépendance spatiale utilisée par RRF. Enfin, nous proposons une nouvelle spécialisation des RRF appelé "Light Random Regression Forests" pour améliorant l'empreinte mémoire et l'efficacité calculatoire
Locating 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
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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.

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Книги з теми "Multi-Organ"

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D, Higgins Robert S., ed. The multi-organ donor: Selection and management. Abingdon, Oxon, England: Blackwell Science, 1997.

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Canadian Symposium on Multi-Organ Transplantation (1st 1988 University Hospital, London, Ont.). First Canadian symposium on multi-organ transplantation. Edited by Grant David R, Wall William J, Kucherawy Mary Ann, University of Western Ontario, University Hospital (London, Ont.), and Canadian Symposium on Multi-Organ Transplantation. London, Ont: SCITEX Publications, 1989.

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Szema, Anthony M., ed. 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.

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Leeds Teaching Hospitals NHS Trust., Bradford Hospitals NHS Trust. Renal Unit., and Great Britain. Department of Health., eds. 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.

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Davis, Stephanie Duggins, Margaret Rosenfeld, and James Chmiel. Cystic Fibrosis: A Multi-Organ System Approach. Springer International Publishing AG, 2021.

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Davis, Stephanie Duggins, Margaret Rosenfeld, and James Chmiel. Cystic Fibrosis: A Multi-Organ System Approach. Humana, 2020.

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Baldwin, John, Juan Sanchez, and Marc Lorber. The Multi-Organ Donor: Selection and Management. Blackwell Science, Ltd. (UK), 1997.

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8

Mitchell P., M.D. Fink. The Pathophysiology of Sepsis and Multi-Organ Failure. Chapman & Hall, 1997.

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9

World Trade Center Pulmonary Diseases and Multi-Organ System Manifestations. Springer, 2017.

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10

Szema, Anthony M. World Trade Center Pulmonary Diseases and Multi-Organ System Manifestations. Springer, 2018.

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Частини книг з теми "Multi-Organ"

1

Nasreen, Fahtiha, Tasnia Noor Salim, Avishek Kunda, Ibad Ur Rehman, Maryam Aman, and Rebecca Caruana. "Multi-organ Transplantation." In Heart Transplantation, 231–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17311-0_13.

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Marik, Paul Ellis. "Multi-organ Dysfunction Syndrome." In 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.

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Klingemann, H. G. "Single and Multi-Organ Failure." In 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.

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Dawahra, M., X. Martin, P. Cloix, L. Tajra, and J. M. Dubernard. "Surgical optimization of multi-organ procurement." In Organ Shortage: The Solutions, 197–205. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0201-8_26.

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Chen, Zhonghua, Tapani Ristaniemi, Fengyu Cong, and Hongkai Wang. "Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling." In 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.

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Klimas, Natasha, Josephine Quintanilla-Dieck, and Travis Vandergriff. "Drug-Induced Delayed Multi-organ Hypersensitivity Syndrome." In Cutaneous Drug Eruptions, 271–79. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6729-7_25.

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Druml, W. "Multi-Organ-Versagen: das Versagen der Zelle." In Multiorganversagen, 1–8. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-9201-6_1.

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Cassimjee, Sarah. "Critical Care, Respiratory and Multi-Organ Failure." In Nutrition and HIV, 427–41. West Sussex, UK: John Wiley & Sons Ltd., 2013. http://dx.doi.org/10.1002/9781118786529.ch21.

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Song, Haobo, Chang Liu, Lukas Folle, and Andreas Maier. "Multi-organ Segmentation with Partially Annotated Datasets." In Informatik aktuell, 216–21. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36932-3_46.

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Okada, Toshiyuki, Keita Yokota, Masatoshi Hori, Masahiko Nakamoto, Hironobu Nakamura, and Yoshinobu Sato. "Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images." In 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.

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Тези доповідей конференцій з теми "Multi-Organ"

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Goëau, Hervé, Pierre Bonnet, Julien Barbe, Vera Bakic, Alexis Joly, Jean-François Molino, Daniel Barthelemy, and Nozha Boujemaa. "Multi-organ plant identification." In the 1st ACM international workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2390832.2390843.

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Lee, Sue Han, Yang Loong Chang, Chee Seng Chan, and Paolo Remagnino. "HGO-CNN: Hybrid generic-organ convolutional neural network for multi-organ plant classification." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8297126.

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Okada, T., M. G. Linguraru, M. Hori, Y. Suzuki, R. M. Summers, N. Tomiyama, and Y. Sato. "Multi-organ segmentation in abdominal CT images." In 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.

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Cantemir-Stone, Carmen Z., Nathan Marzlin, Jie Zhang, Kevin Wenzke, Kun Huang, and Clay B. Marsh. "Unique Gene Signature For Multi-Organ Fibrosis." In 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.

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Ali, Asfand Yar, and Labiba Gillani Fahad. "Multi-Organ Plant Classification Using Deep Learning." In 2022 24th International Multitopic Conference (INMIC). IEEE, 2022. http://dx.doi.org/10.1109/inmic56986.2022.9972979.

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Hu, JiaWen, and Kai Wang. "Abdominal Multi-Organ Segmentation Based on nnUNet." In 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2023. http://dx.doi.org/10.1109/icsp58490.2023.10248829.

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Zhang, Lefei, Shixiang Feng, Yu Wang, Yanfeng Wang, Ya Zhang, Xin Chen, and Qi Tian. "Unsupervised Ensemble Distillation for Multi-Organ Segmentation." In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761568.

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Zhang, Lefei, Shixiang Feng, Yu Wang, Yanfeng Wang, Ya Zhang, Xin Chen, and Qi Tian. "Unsupervised Ensemble Distillation for Multi-Organ Segmentation." In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761568.

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Joyseeree, Ranveer R., and Henning Müller. "Locating seed points for automatic multi-organ segmentation using non-rigid registration and organ annotations." In SPIE Medical Imaging, edited by Sébastien Ourselin and Martin A. Styner. SPIE, 2015. http://dx.doi.org/10.1117/12.2081204.

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Brosch, Tom, and Axel Saalbach. "Foveal fully convolutional nets for multi-organ segmentation." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293528.

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