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Дисертації з теми "Deep image"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

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.

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Deformable image registration is usually performed manually by clinicians,which is time-consuming and costly, or using optimization-based algorithms, which are not always optimal for registering images of different modalities. In this work, a deep learning-based method for MR-CT deformable image registration is presented. In the first place, a neural network is optimized to register CT pelvic image pairs. Later, the model is trained on MR-CT image pairs to register CT images to match its MR counterpart. To solve the unavailability of ground truth data problem, two approaches were used. For the

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2

Ma, Sihan. "Image Matting via Deep Learning." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22426.

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Image matting aims to extract the accurate opacity of the foreground from the input RGB image, which is beneficial and essential for the subsequent applications, such as image editing, compositing, and film production. Unfortunately, this task is challenging because of its ill-posed nature. Specifically, with the corresponding foreground and background unknown, it is hard to predict the opacity of the foreground from the single RGB image. Recently, deep learning is introduced to image matting to deal with this problem. However, there are still some issues to be addressed. First, the choice of

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3

Dumas, Thierry. "Deep learning for image compression." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S029/document.

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Ces vingt dernières années, la quantité d’images et de vidéos transmises a augmenté significativement, ce qui est principalement lié à l’essor de Facebook et Netflix. Même si les capacités de transmission s’améliorent, ce nombre croissant d’images et de vidéos transmises exige des méthodes de compression plus efficaces. Cette thèse a pour but d’améliorer par l’apprentissage deux composants clés des standards modernes de compression d’image, à savoir la transformée et la prédiction intra. Plus précisément, des réseaux de neurones profonds sont employés car ils ont un grand pouvoir d’approximati

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4

Siarohin, Aliaksandr. "Image Animation Using Deep Learning." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/310291.

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Recently media content generation, particularly image and video, using deep learning gained a lot of attention in the research community. One of the main reasons for that is the surge of the interactions in the social networks, that draw a lot of people without specialized backgrounds into the media industry. This raises the interest in the tolls for simplifying the production of the media content, such as images and videos. Another potential avenue for deep learning methods is a simplification of the content generation for the traditional media, especially creation of movies, visual effects f

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5

Siarohin, Aliaksandr. "Image Animation Using Deep Learning." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/310291.

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Recently media content generation, particularly image and video, using deep learning gained a lot of attention in the research community. One of the main reasons for that is the surge of the interactions in the social networks, that draw a lot of people without specialized backgrounds into the media industry. This raises the interest in the tolls for simplifying the production of the media content, such as images and videos. Another potential avenue for deep learning methods is a simplification of the content generation for the traditional media, especially creation of movies, visual effects f

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6

Zhang, Edwin Meng. "Image Miner : an architecture to support deep mining of images." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100612.

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Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 69-70).<br>In this thesis, I designed a cloud based system, called ImageMiner, to tune parameters of feature extraction process in a machine learning pipeline for images. Feature extractio

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7

Dunlop, J. S., R. J. McLure, A. D. Biggs, et al. "A deep ALMA image of the Hubble Ultra Deep Field." OXFORD UNIV PRESS, 2017. http://hdl.handle.net/10150/623849.

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We present the results of the first, deep Atacama Large Millimeter Array ( ALMA) imaging covering the full similar or equal to 4.5 arcmin(2) of the Hubble Ultra Deep Field ( HUDF) imaged with Wide Field Camera 3/IR on HST. Using a 45-pointing mosaic, we have obtained a homogeneous 1.3-mm image reaching sigma 1.3 similar or equal to 35 mu Jy, at a resolution of similar or equal to 0.7 arcsec. From an initial list of similar or equal to 50 > 3.5 sigma peaks, a rigorous analysis confirms 16 sources with S-1.3 > 120 mu Jy. All of these have secure galaxy counterparts with robust redshifts (< z > =

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8

Zeledon, Lostalo Emilia Maria. "FMRI IMAGE REGISTRATION USING DEEP LEARNING." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2641.

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fMRI imaging is considered key on the understanding of the brain and the mind, for this reason has been the subject of tremendous research connecting different disciplines. The intrinsic complexity of this 4-D type of data processing and analysis has been approached with every single computational perspective, lately increasing the trend to include artificial intelligence. One step critical on the fMRI pipeline is image registration. A model of Deep Networks based on Fully Convolutional Neural Networks, spatial transformation neural networks with a self-learning strategy was proposed for the i

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9

Hossain, Md Zakir. "Deep learning techniques for image captioning." Thesis, Hossain, Md. Zakir (2020) Deep learning techniques for image captioning. PhD thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/60782/.

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Generating a description of an image is called image captioning. Image captioning is a challenging task because it involves the understanding of the main objects, their attributes, and their relationships in an image. It also involves the generation of syntactically and semantically meaningful descriptions of the images in natural language. A typical image captioning pipeline comprises an image encoder and a language decoder. Convolutional Neural Networks (CNNs) are widely used as the encoder while Long short-term memory (LSTM) networks are used as the decoder. A variety of LSTMs and CNNs incl

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10

Tensmeyer, Christopher Alan. "Deep Learning for Document Image Analysis." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7389.

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Анотація:

Automatic machine understanding of documents from image inputs enables many applications in modern document workflows, digital archives of historical documents, and general machine intelligence, among others. Together, the techniques for understanding document images comprise the field of Document Image Analysis (DIA). Within DIA, the research community has identified several sub-problems, such as page segmentation and Optical Character Recognition (OCR). As the field has matured, there has been a trend of moving away from heuristic-based methods, designed for particular tasks and domains of

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11

Grechka, Asya. "Image editing with deep neural networks." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS683.pdf.

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L'édition d'images a une histoire riche remontant à plus de deux siècles. Cependant, l'édition "classique" des images requiert une grande maîtrise artistique et nécessitent un temps considérable, souvent plusieurs heures, pour modifier chaque image. Ces dernières années, d'importants progrès dans la modélisation générative ont permis la synthèse d'images réalistes et de haute qualité. Toutefois, l'édition d'une image réelle est un vrai défi nécessitant de synthétiser de nouvelles caractéristiques tout en préservant fidèlement une partie de l'image d'origine. Dans cette thèse, nous explorons di

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12

Quan, Weize. "Detection of computer-generated images via deep learning." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT076.

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Avec les progrès des outils logiciels d'édition et de génération d'images, il est devenu plus facile de falsifier le contenu des images ou de créer de nouvelles images, même pour les novices. Ces images générées, telles que l'image de rendu photoréaliste et l'image colorisée, ont un réalisme visuel de haute qualité et peuvent potentiellement menacer de nombreuses applications importantes. Par exemple, les services judiciaires doivent vérifier que les images ne sont pas produites par la technologie de rendu infographique, les images colorisées peuvent amener les systèmes de reconnaissance / sur

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13

Li, Yuanxi. "Semantic image similarity based on deep knowledge for effective image retrieval." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/99.

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Анотація:

A flourishing World Wide Web dramatically increases the amount of images uploaded and shared, and exploring them is an interesting and challenging task. While content-based image retrieval, which is based on the low level features extracted from images, has grown relatively mature, human users are more interested in the semantic concepts behind or inside the images. Search that is based solely on the low level features would not be able to satisfy users requirements and not e.ective enough. In order to measure the semantic similarity among images and increase the accuracy of Web image retrie

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14

Al-Bander, B. Q. "Retinal image analysis based on deep learning." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3022573/.

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15

Gajić, Bojana. "Training strategies for efficient deep image retrieval." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673961.

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En aquesta tesi ens centrem en la recuperació i re-identificació d’imatges. L’entrenament de xarxes neuronals profundes usant funcions de pèrdua basades en rànquing ha esdevingut un estàndard de facto per a les tasques de recuperació i re-identificació. Hi analitzem i aportem propostes de respostes a tres qüestions principals: 1) Quines són les estratègies més rellevants dels mètodes de l’estat de l’art i com es poden combinar per obtenir un millor rendiment? 2) Es pot realitzar un mostreig de mostres negatives restrictiu de manera eficient (O(1)) mentre es proporciona un rendiment millorat re

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16

Chen, He. "Microscopic Hyperspectral Image Analysis via Deep Learning." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/396188.

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Hyperspectral imaging (HSI) is a technique that can obtain more spectral information than that in normal color images. Due to this property and strength in material classification, it is widely used in remote sensing, agriculture, and environmental monitoring. In recent years, with the rapid developments of hardware, hyperspectral cameras have become more portable and a ordable. An increasing number of studies are being conducted on HSI systems, and research focuses have expanded from remote sensing to close-range objects. With a proper microscopic kit, a hyperspectral camera can capture image

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17

Shermin, Tasfia. "Enhancing deep transfer learning for image classification." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179551.

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Though deep learning models require a large amount of labelled training data for yielding high performance, they are applied to accomplish many computer vision tasks such as image classification. Current models also do not perform well across different domain settings such as illumination, camera angle and real-to-synthetic. Thus the models are more likely to misclassify unknown classes as known classes. These issues challenge the supervised learning paradigm of the models and encourage the study of transfer learning approaches. Transfer learning allows us to utilise the knowledge acquired fro

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18

Nugroho, Bayu Adhi. "Advanced Deep Learning for Medical Image Analysis." Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/87912.

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The application of deep learning is evolving, including in expert systems for healthcare, such as disease classification. Several challenges in the use of deep-learning algorithms in application to disease classification. The study aims to improve classification to address the problem. The thesis proposes a cost-sensitive imbalance training algorithm to address an unequal number of training examples, a two-stage Bayesian optimisation training algorithm and a dual-branch network to train a one-class classification scheme, further improving classification performance.

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19

Altaf, Fouzia. "Deep learning augmentation for medical image analysis." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2603.

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Deep learning is at the center of the current rise of computer aided diagnosis in medical imaging. This technology has the ability to mimic extremely complex mathematical functions for predictive tasks. These functions are encoded as computational models that are learned directly from data. Deep learning models are known to achieve human-level accuracy for predictive tasks. However, such a performance requires that the model is trained on a huge amount of training data. For computer aided diagnosis tasks, the relevant training data needs to be carefully annotated by medical experts. This proce

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20

Gonfaus, Josep M. "Towards Deep Image Understanding: From pixels to semantics." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/117584.

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Entendre el contingut de les imatges és un dels grans reptes de la visió per computador. Arribar a ser capaços de reconèixer quins objectes apareixen en les imatges, quina acció hi realitzen, i finalment, entendre el per què esta succeïnt, és l'objectiu del topic de Image Understanding. El fet d'entendre què succeeix en un instant de temps, ja sigui capturat en una fotografia, en un vídeo o simplement la imatge retinguda en la retina de l'ull (humà o un robòtic) és un pas fonamental per tal de formar-n'hi part. Per exemple, per un robot o un cotxe intel·ligent, es imprescindible de reconèixer

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21

Pitkänen, P. (Perttu). "Automatic image quality enhancement using deep neural networks." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201904101454.

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Abstract. Photo retouching can significantly improve image quality and it is considered an essential part of photography. Traditionally this task has been completed manually with special image enhancement software. However, recent research utilizing neural networks has been proven to perform better in the automated image enhancement task compared to traditional methods. During the literature review of this thesis, multiple automatic neural-network-based image enhancement methods were studied, and one of these methods was chosen for closer examination and evaluation. The chosen network desig

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22

Kang, Le. "Document and natural image applications of deep learning." Thesis, University of Maryland, College Park, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3726222.

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<p> A tremendous amount of digital visual data is being collected every day, and we need efficient and effective algorithms to extract useful information from that data. Considering the complexity of visual data and the expense of human labor, we expect algorithms to have enhanced generalization capability and depend less on domain knowledge. While many topics in computer vision have benefited from machine learning, some document analysis and image quality assessment problems still have not found the best way to utilize it. In the context of document images, a compelling need exists for reliab

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23

Ahn, Euijoon. "Unsupervised Deep Feature Learning for Medical Image Analysis." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23002.

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The availability of annotated image datasets and recent advances in supervised deep learning methods are enabling the end-to-end derivation of representative image features that can impact a variety of image analysis problems. These supervised methods use prior knowledge derived from labelled training data and approaches, for example, convolutional neural networks (CNNs) have produced impressive results in natural (photographic) image classification. CNNs learn image features in a hierarchical fashion. Each deeper layer of the network learns a representation of the image data that is higher le

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24

Chai, Zhizhi. "Deep learning training optimization for medical image analysis." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24928.

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A computer-aided diagnosis (CAD) system can automatically analyze, quantify, and categorize medical images. Therefore, CAD systems are widely used in patient care. However, current CAD systems are expert-designed with limited capabilities. Recently, deep convolutional neural networks (DCNNs) have achieved great success in automating many medical image interpretation such as lesion segmentation and medical disease classification. These successes are attributed to DCNN’s abilities to automatically learn useful image feature from a large number of annotated images. However, due to complicated dat

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25

Neupane, Aashish. "Visual Saliency Analysis on Fashion Images Using Image Processing and Deep Learning Approaches." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2784.

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ABSTRACTAASHISH NEUPANE, for the Master of Science degree in BIOMEDICAL ENGINEERING, presented on July 35, 2020, at Southern Illinois University Carbondale. TITLE: VISUAL SALIENCY ANALYSIS ON FASHION IMAGES USING IMAGE PROCESSING AND DEEP LEARNING APPROACHES.MAJOR PROFESSOR: Dr. Jun QinState-of-art computer vision technologies have been applied in fashion in multiple ways, and saliency modeling is one of those applications. In computer vision, a saliency map is a 2D topological map which indicates the probabilistic distribution of visual attention priorities. This study is focusing on analysis

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26

Rodés-Guirao, Lucas. "Deep Learning for Digital Typhoon : Exploring a typhoon satellite image dataset using deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249514.

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Efficient early warning systems can help in the management of natural disaster events, by allowing for adequate evacuations and resources administration. Several approaches have been used to implement proper early warning systems, such as simulations or statistical models, which rely on the collection of meteorological data. Data-driven techniques have been proven to be effective to build statistical models, being able to generalise to unseen data. Motivated by this, in this work, we explore deep learning techniques applied to the typhoon meteorological satellite image dataset "Digital Typhoon

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27

PONZIO, FRANCESCO. "Deep learning at the microscope - Working towards improved microscopy image analysis with deep neural networks." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2935596.

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28

Le, Van Linh. "Automatic landmarking for 2D biological images : image processing with and without deep learning methods." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0238.

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Les points de repère sont présentés dans les applications de différents domaines tels que le biomédical ou le biologique. C’est également l’un des types de données qui ont été utilisés dans différentes analyses, par exemple, ils ne sont pas seulement utilisés pour mesurer la forme de l’objet, mais également pour déterminer la similarité entre deux objets. En biologie, les repères sont utilisés pour analyser les variations inter-organismes. Cependant, l’offre de repères est très lourde et le plus souvent, ils sont fournis manuellement. Ces dernières années, plusieurs méthodes ont été proposées

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29

Rontauroli, Matteo. "Soluzioni di Deep Learning in ambito di Image Steganography." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20485/.

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La steganografia è la pratica del nascondere un messaggio segreto all’interno di una comunicazione ordinaria. Le sfide in tal senso sono legate ad un’immissione del messaggio che permetta al destinatario di decodificarlo e che allo stesso tempo non sia sospettabile da una parte terza. Nel caso di questo lavoro viene considerata l’incorporazione del messaggio all’interno di immagini, in particolare per due differenti casi, attraverso tecniche di Deep Learning. Nel primo il messaggio segreto è un’immagine delle stesse dimensioni di quella nella quale deve essere incorporata e la trasmissione e

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30

Alpi, Davide. "Metodi di Deep Learning per Blind Image Quality Assessment." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23084/.

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Le tecniche di Deep Learning per Blind Image Quality Assessment consistono nella progettazione e sviluppo di reti neurali profonde per l'assegnazione di punteggi di qualità alle immagini. In questa tesi viene per prima cosa svolta un'indagine sugli approcci al problema presentati in letteratura. Viene presentato brevemente il funzionamento delle reti neurali convoluzionali e elencati dei modelli di successo, evidenziando per ciascuno le novità architetturali introdotte e i traguardi ottenuti in termini di prestazioni. Si procede poi con la progettazione di diversi modelli single-task e multi-t

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31

Martinez-Covarrubias, Julieta. "Compositional compression of deep image features using stacked quantizers." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/51511.

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In computer vision, it is common for image representations to be stored as high-dimensional real-valued vectors. In many computer vision applications, such as retrieval, classification, registration and reconstruction, the computational bottleneck arises in a process known as feature matching, where, given a query vector, a similarity score has to be computed to many vectors in a (potentially very large) database. For example, it is not uncommon for object retrieval and classification to be performed by matching global representations in collections with thousands or millions of images. A pop

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32

Ishaq, Omer. "Image Analysis and Deep Learning for Applications in Microscopy." Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-283846.

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Quantitative microscopy deals with the extraction of quantitative measurements from samples observed under a microscope. Recent developments in microscopy systems, sample preparation and handling techniques have enabled high throughput biological experiments resulting in large amounts of image data, at biological scales ranging from subcellular structures such as fluorescently tagged nucleic acid sequences to whole organisms such as zebrafish embryos. Consequently, methods and algorithms for automated quantitative analysis of these images have become increasingly important. These methods range

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33

Hou, Xianxu. "An investigation of deep learning for image processing applications." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52056/.

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Significant strides have been made in computer vision over the past few years due to the recent development in deep learning, especially deep convolutional neural networks (CNNs). Based on the advances in GPU computing, innovative model architectures and large-scale dataset, CNNs have become the workhorse behind the state of the art performance for most computer vision tasks. For instance, the most advanced deep CNNs are able to achieve and even surpass human-level performance in image classification tasks. Deep CNNs have demonstrated the ability to learn very powerful image features or repres

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34

Wang, Wei. "Image Segmentation Using Deep Learning Regulated by Shape Context." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227261.

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In recent years, image segmentation by using deep neural networks has made great progress. However, reaching a good result by training with a small amount of data remains to be a challenge. To find a good way to improve the accuracy of segmentation with limited datasets, we implemented a new automatic chest radiographs segmentation experiment based on preliminary works by Chunliang using deep learning neural network combined with shape context information. When the process was conducted, the datasets were put into origin U-net at first. After the preliminary process, the segmented images were

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35

Ahmed, Mohamed. "Medical Image Segmentation using Attention-Based Deep Neural Networks." Thesis, KTH, Medicinsk avbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284224.

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During the last few years, segmentation architectures based on deep learning achieved promising results. On the other hand, attention networks have been invented years back and used in different tasks but rarely used in medical applications. This thesis investigated four main attention mechanisms; Squeeze and Excitation, Dual Attention Network, Pyramid Attention Network, and Attention UNet to be used in medical image segmentation. Also, different hybrid architectures proposed by the author were tested. Methods were tested on a kidney tumor dataset and against UNet architecture as a baseline. O

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Pourebadi, Maryam. "A DEEP LEARNING APPROACH FOR BLIND IMAGE QUALITY ASSESSMENT." Kent State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1499797575099571.

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Chen, Yani. "Deep Learning based 3D Image Segmentation Methods and Applications." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1547066297047003.

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Kang, Chen. "Image Aesthetic Quality Assessment Based on Deep Neural Networks." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG004.

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Avec le développement des dispositifs de capture et d'Internet, les gens accèdent à un nombre croissant d'images. L'évaluation de l'esthétique visuelle a des applications importantes dans plusieurs domaines, de la récupération d'image et de la recommandation à l'amélioration. L'évaluation de la qualité esthétique de l'image vise à déterminer la beauté d'une image pour les observateurs humains. De nombreux problèmes dans ce domaine ne sont pas bien étudiés, y compris la subjectivité de l'évaluation de la qualité esthétique, l'explication de l'esthétique et la collecte de données annotées par l'

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Khan, Muhammad Faisal. "Non-rigid image registration for deep brain stimulation surgery." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26546.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Oskar krinjar; Committee Member: Allen Tannenbaum; Committee Member: Anthony Yezzi; Committee Member: John Oshinski; Committee Member: Patricio Vela. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.

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Biomedical image analysis plays a crucial role in the development of healthcare, with a wide scope of applications including the disease diagnosis, clinical treatment, and future prognosis. Among various biomedical image analysis techniques, segmentation is an essential step, which aims at assigning each pixel with labels of interest on the category and instance. At the early stage, the segmentation results were obtained via manual annotation, which is time-consuming and error-prone. Over the past few decades, hand-craft feature based methods have been proposed to segment the biomedical images

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Bi, Lei. "Deep Cascaded Fully Convolutional Networks for Medical Image Segmentation." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18719.

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Segmentation of regions of interest (ROIs) in medical images is an important step for image analysis in computer aided diagnosis (CAD) systems. Recently, deep learning methods based on fully convolutional networks (FCN) have achieved great success in segmentation tasks on natural images. This success is primarily attributed to the ability of the FCN to leverage large datasets to hierarchically learn the features that best correspond to the appearance as well as the semantics of the images. However, there is a scarcity of annotated medical image training data due to the large cost and complicat

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Li, Qing. "Medical image analysis with neural network and deep learning." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/14940.

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Thanks to the advance of biomedical imaging systems, large volumes of biomedical image data are generated rapidly. However many doctors still rely on time consuming manual inspection of the patients’ medical images to make diagnostic decisions. There is growing interest from medical and biology researchers to develop automated tools and algorithms that can efficiently extract and utilize useful information from large scale biomedical image databases. Artificial Neural Networks (ANN) have proved very successful in various machine learning and artificial intelligence areas. Recent advances in n

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Huang, Ruobing. "Delving deep into fetal neurosonography : an image analysis approach." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:63aec035-dee2-40d4-9e00-ee1674a52494.

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Ultrasound screening has been used for decades as the main modality to examine fetal brain development and to diagnose possible anomalies. However, basic clinical ultrasound examination of the fetal head is limited to axial planes of the brain and linear measurements which may have restrained its potential and efficacy. The recent introduction of three-dimensional (3D) ultrasound provides the opportunity to navigate to different anatomical planes and to evaluate structures in 3D within the developing brain. Regardless of acquisition methods, interpreting 2D/3D ultrasound fetal brain images req

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Gidaris, Spyridon. "Effective and annotation efficient deep learning for image understanding." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1143/document.

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Le développement récent de l'apprentissage profond a permis une importante amélioration des résultats dans le domaine de l'analyse d'image. Cependant, la conception d'architectures d'apprentissage profond à même de résoudre efficacement les tâches d'analyse d'image est loin d'être simple. De plus, le succès des approches d'apprentissage profond dépend fortement de la disponibilité de données en grande quantité étiquetées manuellement (par des humains), ce qui est à la fois coûteux et peu pratique lors du passage à grande échelle. Dans ce contexte, l'objectif de cette thèse est d'explorer des a

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Malek, Salim. "Deep neural network models for image classification and regression." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/368992.

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Deep learning, a branch of machine learning, has been gaining ground in many research fields as well as practical applications. Such ongoing boom can be traced back mainly to the availability and the affordability of potential processing facilities, which were not widely accessible than just a decade ago for instance. Although it has demonstrated cutting-edge performance widely in computer vision, and particularly in object recognition and detection, deep learning is yet to find its way into other research areas. Furthermore, the performance of deep learning models has a strong dependency on t

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Malek, Salim. "Deep neural network models for image classification and regression." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2961/1/These_final02.pdf.

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Deep learning, a branch of machine learning, has been gaining ground in many research fields as well as practical applications. Such ongoing boom can be traced back mainly to the availability and the affordability of potential processing facilities, which were not widely accessible than just a decade ago for instance. Although it has demonstrated cutting-edge performance widely in computer vision, and particularly in object recognition and detection, deep learning is yet to find its way into other research areas. Furthermore, the performance of deep learning models has a strong dependency on t

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Estienne, Théo. "Deep learning-based methods for 3D medical image registration." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG055.

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Cette thèse se concentre sur des nouvelles approches d'apprentissage profond (aussi appelé deep learning) pour trouver le meilleur déplacement entre deux images médicales différentes. Ce domaine de recherche, appelé recalage d'images, a de nombreuses applications dans la prise en charge clinique, notamment la fusion de différents types d'imagerie ou le suivi temporel d'un patient. Ce domaine est étudié depuis de nombreuses années avec diverses méthodes, telles que les méthodes basées sur des difféomorphismes, sur des graphes ou sur des équations physiques. Récemment, des méthodes basées sur l'

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Berthet, Alexandre. "Deep learning methods and advancements in digital image forensics." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS252.

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Le volume de données visuelles numériques augmente considérablement d'année en années. En parallèle, l’édition d'images est devenue plus facile et plus précise. Les modifications malveillantes sont donc plus accessibles. La criminalistique des images fournit des solutions pour garantir l’authenticité des données visuelles numériques. Tout d’abord, les solutions étaient des méthodes classiques basées sur les artéfacts produits lors de la création d’une image numérique. Puis, comme pour d’autres domaines du traitement d’images, les méthodes sont passées à l’apprentissage profond. Dans un premier

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"Deep Learning for Image Restoration." 2016. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1292595.

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Barbosa, Bruno Miguel da Silva. "Image recognition using deep learning." Master's thesis, 2018. http://hdl.handle.net/1822/59733.

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Dissertação de mestrado em Computer Science<br>Computer vision is a vast knowledge subject responsible for traducing digital images and videos into a higher level of understandable information. Image recognition is one of the several tasks that are inserted in this subject and it can be subdivided in object recognition (also called as object classification), segmentation, identification and detection. Some of the available alternatives for image recognition are based on Machine Learning (ML) approaches. Deep Learning (DL) is a branch of ML that became very popular in the last years due t

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