Дисертації з теми "Deep Learning Imaging"
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Li, Shuai Ph D. Massachusetts Institute of Technology. "Computational imaging through deep learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122070.
Повний текст джерелаThesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 143-154).
Computational imaging (CI) is a class of imaging systems that uses inverse algorithms to recover an unknown object from the physical measurement. Traditional inverse algorithms in CI obtain an estimate of the object by minimizing the Tikhonov functional, which requires explicit formulations of the forward operator of the physical system, as well as the prior knowledge about the class of objects being imaged. In recent years, machine learning architectures, and deep learning (DL) in particular, have attracted increasing attentions from CI researchers. Unlike traditional inverse algorithms in CI, DL approach learns both the forward operator and the objects' prior implicitly from training examples. Therefore, it is especially attractive when the forward imaging model is uncertain (e.g. imaging through random scattering media), or the prior about the class of objects is difficult to be expressed analytically (e.g. natural images).
In this thesis, the application of DL approaches in two different CI scenarios are investigated: imaging through a glass diffuser and quantitative phase retrieval (QPR), where an Imaging through Diffuser Network (IDiffNet) and a Phase Extraction Neural Network (PhENN) are experimentally demonstrated, respectively. This thesis also studies the influences of the two main factors that determine the performance of a trained neural network: network architecture (connectivity, network depth, etc) and training example quality (spatial frequency content in particular). Motivated by the analysis of the latter factor, two novel approaches, spectral pre-modulation approach and Learning Synthesis by DNN (LS-DNN) method, are successively proposed to improve the visual qualities of the network outputs. Finally, the LS-DNN enhanced PhENN is applied to a phase microscope to recover the phase of a red blood cell (RBC) sample.
Furthermore, through simulation of the learned weak object transfer function (WOTF) and experiment on a star-like phase target, we demonstrate that our network has indeed learned the correct physical model rather than doing something trivial as pattern matching.
by Shuai Li.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineering
Alzubaidi, Laith. "Deep learning for medical imaging applications." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227812/1/Laith_Alzubaidi_Thesis.pdf.
Повний текст джерелаBernal, Moyano Jose. "Deep learning for atrophy quantification in brain magnetic resonance imaging." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/671699.
Повний текст джерелаLa cuantificación de la atrofia cerebral es fundamental en la neuroinformática ya que permite diagnosticar enfermedades cerebrales, evaluar su progresión y determinar la eficacia de los nuevos tratamientos para contrarrestarlas. Sin embargo, éste sigue siendo un problema abierto y difícil, ya que el rendimiento de los métodos tradicionales depende de los protocolos y la calidad de las imágenes, los errores de armonización de los datos y las anomalías del cerebro. En esta tesis doctoral, cuestionamos si los métodos de aprendizaje profundo pueden ser utilizados para estimar mejor la atrofia cerebral a partir de imágenes de resonancia magnética. Nuestro trabajo muestra que el aprendizaje profundo puede conducir a un rendimiento de vanguardia en las evaluaciones transversales y competir y superar los métodos tradicionales de cuantificación de la atrofia longitudinal. Creemos que los métodos transversales y longitudinales propuestos pueden ser beneficiosos para la comunidad investigadora y clínica
Sundman, Tobias. "Noise Reduction in Flash X-ray Imaging Using Deep Learning." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355731.
Повний текст джерелаForsgren, Edvin. "Deep Learning to Enhance Fluorescent Signals in Live Cell Imaging." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-175328.
Повний текст джерелаMcCamey, Morgan R. "Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904.
Повний текст джерелаWajngot, David. "Improving Image Quality in Cardiac Computed Tomography using Deep Learning." Thesis, Linköpings universitet, Avdelningen för kardiovaskulär medicin, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154506.
Повний текст джерелаNie, Yali. "Automatic Melanoma Diagnosis in Dermoscopic Imaging Base on Deep Learning System." Licentiate thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-41751.
Повний текст джерелаHoffmire, Matthew A. "Deep Learning for Anisoplanatic Optical Turbulence Mitigation in Long Range Imaging." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607694391536891.
Повний текст джерелаMarini, Michela. "Representation learning and applications in neuronal imaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19776/.
Повний текст джерелаNasrin, Mst Shamima. "Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676.
Повний текст джерелаWallis, David. "A study of machine learning and deep learning methods and their application to medical imaging." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST057.
Повний текст джерелаWe first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models. We first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models
Vekhande, Swapnil Sudhir. "Deep Learning Neural Network-based Sinogram Interpolation for Sparse-View CT Reconstruction." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90182.
Повний текст джерелаMaster of Science
Computed Tomography is a commonly used imaging technique due to the remarkable ability to visualize internal organs, bones, soft tissues, and blood vessels. It involves exposing the subject to X-ray radiation, which could lead to cancer. On the other hand, the radiation dose is critical for the image quality and subsequent diagnosis. Thus, image reconstruction using only a small number of projection data is an open research problem. Deep learning techniques have already revolutionized various Computer Vision applications. Here, we have used a method which fills missing highly sparse CT data. The results show that the deep learning-based method outperforms standard linear interpolation-based methods while improving the image quality.
Sahasrabudhe, Mihir. "Unsupervised and weakly supervised deep learning methods for computer vision and medical imaging." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASC010.
Повний текст джерелаThe first two contributions of this thesis (Chapter 2 and 3) are models for unsupervised 2D alignment and learning 3D object surfaces, called Deforming Autoencoders (DAE) and Lifting Autoencoders (LAE). These models are capable of identifying canonical space in order to represent different object properties, for example, appearance in a canonical space, deformation associated with this appearance that maps it to the image space, and for human faces, a 3D model for a face, its facial expression, and the angle of the camera. We further illustrate applications of models to other domains_ alignment of lung MRI images in medical image analysis, and alignment of satellite images for remote sensing imagery. In Chapter 4, we concentrate on a problem in medical image analysis_ diagnosis of lymphocytosis. We propose a convolutional network to encode images of blood smears obtained from a patient, followed by an aggregation operation to gather information from all images in order to represent them in one feature vector which is used to determine the diagnosis. Our results show that the performance of the proposed models is at-par with biologists and can therefore augment their diagnosis
Cabrera, Gil Blanca. "Deep Learning Based Deformable Image Registration of Pelvic Images." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279155.
Повний текст джерелаChen, Zhiang. "Deep-learning Approaches to Object Recognition from 3D Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496303868914492.
Повний текст джерелаRen, Jing. "From RF signals to B-mode Images Using Deep Learning." Thesis, KTH, Medicinteknik och hälsosystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235061.
Повний текст джерелаHellström, Terese. "Deep-learning based prediction model for dose distributions in lung cancer patients." Thesis, Stockholms universitet, Fysikum, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-196891.
Повний текст джерелаKostopouls, Theodore P. "A Machine Learning approach to Febrile Classification." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1173.
Повний текст джерелаMaestri, Rita. "Metodiche di deep learning e applicazioni all’imaging medico: la radiomica." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15452/.
Повний текст джерелаNayak, Aman Kumar. "Segmenting the Left Atrium in Cardic CT Images using Deep Learning." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176592.
Повний текст джерелаCamborata, Caterina. "Capsule networks: a new approach for brain imaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18127/.
Повний текст джерелаRan, Peipei. "Imaging and diagnostic of sub-wavelength micro-structures, from closed-form algorithms to deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG061.
Повний текст джерелаElectromagnetic probing of a gridlike, finite set of infinitely long circular cylindrical dielectric rods affected by missing ones is investigated from time-harmonic single and multiple frequency data. Sub-wavelength distances between adjacent rods and sub-wavelength rod diameters are assumed throughout the frequency band of operation and this leads to a severe challenge due to need of super-resolution within the present micro-structure, well beyond the Rayleigh criterion. A wealth of solution methods is investigated and comprehensive numerical simulations illustrate pros and cons, completed by processing laboratory-controlled experimental data acquired on a micro-structure prototype in a microwave anechoic chamber. These methods, which differ per a priori information accounted for and consequent versatility, include time-reversal, binary-specialized contrast-source and sparsity-constrained inversions, and convolutional neural networks possibly combined with recurrent ones
Wang, Chuangqi. "Machine Learning Pipelines for Deconvolution of Cellular and Subcellular Heterogeneity from Cell Imaging." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/587.
Повний текст джерелаPech, Thomas Joel. "A Deep-Learning Approach to Evaluating the Navigability of Off-Road Terrain from 3-D Imaging." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496377449249936.
Повний текст джерелаCampanini, Matteo. "Architetture di deep learning per l'imaging medico del tumore alla prostata." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16369/.
Повний текст джерелаLiso, Lorenzo. "Rete Residuale per la Rimozione di Rumore Poissoniano e Gaussiano da Immagini Mediche." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23560/.
Повний текст джерелаRabenius, Michaela. "Deep Learning-based Lung Triage for Streamlining the Workflow of Radiologists." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160537.
Повний текст джерелаSörman, Paulsson Elsa. "Evaluation of In-Silico Labeling for Live Cell Imaging." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-180590.
Повний текст джерелаHrabovszki, Dávid. "Classification of brain tumors in weakly annotated histopathology images with deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177271.
Повний текст джерелаKoppers, Simon [Verfasser], Dorit [Akademischer Betreuer] Merhof, and Thomas [Akademischer Betreuer] Schultz. "Signal enhancement and signal reconstruction for diffusion imaging using deep learning / Simon Koppers ; Dorit Merhof, Thomas Schultz." Aachen : Universitätsbibliothek der RWTH Aachen, 2019. http://d-nb.info/1218727691/34.
Повний текст джерелаMartínez, Mora Andrés. "Automation of Kidney Perfusion Analysis from Dynamic Phase-Contrast MRI using Deep Learning." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277752.
Повний текст джерелаTorrents, Barrena Jordina. "Deep learning -based segmentation methods for computer-assisted fetal surgery." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/668188.
Повний текст джерелаAquesta tesi comprèn el desenvolupament de tècniques de processament d’imatge basades en aprenentatge profund per a la detecció i segmentació d’estructures fetals en imatges de ressonància magnètica (RM) i ultrasò (US) tridimensional d’embarassos normals i de bessons. S’ha fet especial èmfasi en el cas de bessons monocoriònics afectats per la síndrome de transfusió feto fetal (STFF). En aquest context es proposa la primera plataforma de planificació i simulació quirúrgica orientada a STFF. S’han utilitzat diferents mètodes per segmentar automàticament el teixit de la mare, l’úter, la placenta, els seus vasos perifèrics i el cordó umbilical a partir de les diferents vistes en RM o a partir d’un volum en super-resolució. S’han utilitzat xarxes generatives antagòniques (condicionals) per a la segmentació d’estructures en imatges d’US tridimensionals i s’ha localitzat la inserció del cordó a partir d’US Doppler. Finalment, es presenta un estudi comparatiu de les metodologies d’aprenentatge profund i Radiomics.
Tardy, Mickael. "Deep learning for computer-aided early diagnosis of breast cancer." Thesis, Ecole centrale de Nantes, 2021. http://www.theses.fr/2021ECDN0035.
Повний текст джерелаBreast cancer has the highest incidence amongst women. Regular screening allows to reduce the mortality rate, but creates a heavy workload for clinicians. To reduce it, the computer-aided diagnosis tools are designed, but a high level of performances is expected. Deep learning techniques have a potential to overcome the limitations of the traditional image processing algorithms. Although several challenges come with the deep learning applied to breast imaging, including heterogeneous and unbalanced data, limited amount of annotations, and high resolution. Facing these challenges, we approach the problem from multiple angles and propose several methods integrated in complete solution. Hence, we propose two methods for the assessment of the breast density as one of the cancer development risk factors, a method for abnormality detection, a method for uncertainty estimation of a classifier, and a method of transfer knowledge from mammography to tomosynthesis. Our methods contribute to the state of the art of weakly supervised learning and open new paths for further research
Dong, Xu. "Material-Specific Computed Tomography for Molecular X-Imaging in Biomedical Research." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88869.
Повний текст джерелаDoctor of Philosophy
X-ray Computed Tomography (CT) has played a central role in clinical imaging since it was invented in 1972. It has distinguishing characteristics of being able to generate three dimensional images with comprehensive inner structural information in fast speed (less than one second). However, traditional CT imaging lacks of material-specific capability due to the mechanism of image formation, which makes it cannot be used for molecular imaging. Molecular imaging plays a central role in present and future biomedical research and clinical diagnosis and treatment. For example, imaging of biological processes and molecular markers can provide unprecedented rich information, which has huge potentials for individualized therapies, novel drug design, earlier diagnosis, and personalized medicine. Therefore there exists a pressing need to enable the traditional CT imaging technique with material-specific capability for molecular imaging purpose. This dissertation conducted comprehensive study to separately investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging utilizes fluorescence signal to achieve molecular imaging in CT; Material identification can be achieved based on the rich image data from PCCT; The deep learning based correction method is an efficient approach for PCCT data distortion correction, and furthermore can boost its performance on material identification. With those techniques, the material-specific capability of CT can be greatly enhanced and the molecular imaging can be approached in biological bodies.
Singh, Praveer. "Processing high-resolution images through deep learning techniques." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1172.
Повний текст джерелаIn this thesis, we discuss four different application scenarios that can be broadly grouped under the larger umbrella of Analyzing and Processing high-resolution images using deep learning techniques. The first three chapters encompass processing remote-sensing (RS) images which are captured either from airplanes or satellites from hundreds of kilometers away from the Earth. We start by addressing a challenging problem related to improving the classification of complex aerial scenes through a deep weakly supervised learning paradigm. We showcase as to how by only using the image level labels we can effectively localize the most distinctive regions in complex scenes and thus remove ambiguities leading to enhanced classification performance in highly complex aerial scenes. In the second chapter, we deal with refining segmentation labels of Building footprints in aerial images. This we effectively perform by first detecting errors in the initial segmentation masks and correcting only those segmentation pixels where we find a high probability of errors. The next two chapters of the thesis are related to the application of Generative Adversarial Networks. In the first one, we build an effective Cloud-GAN model to remove thin films of clouds in Sentinel-2 imagery by adopting a cyclic consistency loss. This utilizes an adversarial lossfunction to map cloudy-images to non-cloudy images in a fully unsupervised fashion, where the cyclic-loss helps in constraining the network to output a cloud-free image corresponding to the input cloudy image and not any random image in the target domain. Finally, the last chapter addresses a different set of high-resolution images, not coming from the RS domain but instead from High Dynamic Range Imaging (HDRI) application. These are 32-bit imageswhich capture the full extent of luminance present in the scene. Our goal is to quantize them to 8-bit Low Dynamic Range (LDR) images so that they can be projected effectively on our normal display screens while keeping the overall contrast and perception quality similar to that found in HDR images. We adopt a Multi-scale GAN model that focuses on both coarser as well as finer-level information necessary for high-resolution images. The final tone-mapped outputs have a high subjective quality without any perceived artifacts
Alom, Md Zahangir. "Improved Deep Convolutional Neural Networks (DCNN) Approaches for Computer Vision and Bio-Medical Imaging." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1541685818030003.
Повний текст джерелаZhang, Yi. "NOVEL APPLICATIONS OF MACHINE LEARNING IN BIOINFORMATICS." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/83.
Повний текст джерелаBraman, Nathaniel. "Novel Radiomics and Deep Learning Approaches Targeting the Tumor Environment to Predict Response to Chemotherapy." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586546527544791.
Повний текст джерелаAderghal, Karim. "Classification of multimodal MRI images using Deep Learning : Application to the diagnosis of Alzheimer’s disease." Thesis, Bordeaux, 2021. http://www.theses.fr/2021BORD0045.
Повний текст джерелаIn this thesis, we are interested in the automatic classification of brain MRI images to diagnose Alzheimer’s disease (AD). We aim to build intelligent models that provide decisions about a patient’s disease state to the clinician based on visual features extracted from MRI images. The goal is to classify patients (subjects) into three main categories: healthy subjects (NC), subjects with mild cognitive impairment (MCI), and subjects with Alzheimer’s disease (AD). We use deep learning methods, specifically convolutional neural networks (CNN) based on visual biomarkers from multimodal MRI images (structural MRI and DTI), to detect structural changes in the brain hippocampal region of the limbic cortex. We propose an approach called "2-D+e" applied to our ROI (Region-of-Interest): the hippocampus. This approach allows extracting 2D slices from three planes (sagittal, coronal, and axial) of our region by preserving the spatial dependencies between adjacent slices according to each dimension. We present a complete study of different artificial data augmentation methods and different data balancing approaches to analyze the impact of these conditions on our models during the training phase. We propose our methods for combining information from different sources (projections/modalities), including two fusion strategies (early fusion and late fusion). Finally, we present transfer learning schemes by introducing three frameworks: (i) a cross-modal scheme (using sMRI and DTI), (ii) a cross-domain scheme that involves external data (MNIST), and (iii) a hybrid scheme with these two methods (i) and (ii). Our proposed methods are suitable for using shallow CNNs for multimodal MRI images. They give encouraging results even if the model is trained on small datasets, which is often the case in medical image analysis
Rezaei, Mina [Verfasser], Christoph [Akademischer Betreuer] Meinel, Christoph Gutachter] Meinel, Nassir [Gutachter] [Navab, and Heinz [Gutachter] Handels. "Deep representation learning from imbalanced medical imaging / Mina Rezaei ; Gutachter: Christoph Meinel, Nassir Navab, Heinz Handels ; Betreuer: Christoph Meinel." Potsdam : Universität Potsdam, 2019. http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-442759.
Повний текст джерелаBelbaisi, Adham. "Deep Learning-Based Skeleton Segmentation for Analysis of Bone Marrow and Cortical Bone in Water-Fat Magnetic Resonance Imaging." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297528.
Повний текст джерелаRezaei, Mina [Verfasser], Christoph [Akademischer Betreuer] Meinel, Christoph [Gutachter] Meinel, Nassir [Gutachter] Navab, and Heinz [Gutachter] Handels. "Deep representation learning from imbalanced medical imaging / Mina Rezaei ; Gutachter: Christoph Meinel, Nassir Navab, Heinz Handels ; Betreuer: Christoph Meinel." Potsdam : Universität Potsdam, 2019. http://d-nb.info/1218169796/34.
Повний текст джерелаSargent, Garrett Craig. "A Conditional Generative Adversarial Network Demosaicing Strategy for Division of Focal Plane Polarimeters." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1606050550958383.
Повний текст джерелаBerry, Jeffrey James. "Machine Learning Methods for Articulatory Data." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223348.
Повний текст джерелаElsaadouny, Mostafa [Verfasser], Ilona [Gutachter] Rolfes, and Nils [Gutachter] Pohl. "Deep learning models for SAR imaging results interpretation / Mostafa Elsaadouny ; Gutachter: Ilona Rolfes, Nils Pohl ; Fakultät für Elektrotechnik und Informationstechnik." Bochum : Ruhr-Universität Bochum, 2021. http://d-nb.info/1226428592/34.
Повний текст джерелаÖstling, Andreas. "Automated Kidney Segmentation in Magnetic Resonance Imaging using U-Net." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-391269.
Повний текст джерелаKounalakis, Tsampikos. "Depth-adaptive methodologies for 3D image caregorization." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/11531.
Повний текст джерелаLosch, Max. "Detection and Segmentation of Brain Metastases with Deep Convolutional Networks." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173519.
Повний текст джерелаRydell, Christopher. "Deep Learning for Whole Slide Image Cytology : A Human-in-the-Loop Approach." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-450356.
Повний текст джерела