Готові списки джерел за темами / Medical Image Transfer

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Добірка наукової літератури з теми "Medical Image Transfer"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Medical Image Transfer"

Azmi, Azmi, Falath M. M.Mohammed, Saif Al-din M. N, and Azmi Shawkat Abdulbaqi. "Integrating a Secure and Low-Cost WSN Layer with Medical Cloud Computing for Medical Image Transmission." Fusion: Practice and Applications 18, no. 1 (2025): 35–48. https://doi.org/10.54216/fpa.180103.

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Throughout a Wireless Sensor Network (WSN), information collected from the environment is continuously transmitted from one node to the next, and then the main collector or server receives and processes it. With the growth of a network, data transfers within the network also grow dramatically. Medical images increase traffic on a network if they are transmitted. An interlayer transmission protocol (WSN) was developed for this study. Pixels are used to create the medical image using the protocol. A gray-level medical image with 512x512 pixels provided by Brain was used to conduct the study. Med

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John, Siju, and S. N. Kumar. "Medical Image Encryption using Latin Image Cipher Algorithm." Journal of Physics: Conference Series 2327, no. 1 (2022): 012070. http://dx.doi.org/10.1088/1742-6596/2327/1/012070.

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Abstract Image processing has significant applications in the health care sector, medical data processing, analysis, storage, and transfer. The Latin Image Cipher algorithm was proposed in this work for the encryption of medical images. The encryption algorithm proposed in this research work comprises Latin square whitening, substitution, and permutation. The efficiency of the algorithm was also validated by inducing noise in the input images. The performance validation of the proposed algorithm was validated by the histogram analysis and correlation analysis. The information entropy measure a

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Osmani, Nooshin, Sorayya Rezayi, Erfan Esmaeeli, and Afsaneh Karimi. "Transfer Learning from Non-Medical Images to Medical Images Using Deep Learning Algorithms." Frontiers in Health Informatics 13 (January 6, 2024): 177. http://dx.doi.org/10.30699/fhi.v13i0.549.

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Introduction: Machine learning, especially deep convolutional neural networks (DCNNs), is a popular method for computerizing medical image analysis. This study aimed to develop DCNN models for histopathology image classification utilizing transfer learning.Material and Methods: We utilized 16 different pre-trained DCNNs to analyze the histopathology images from the animal diagnostic laboratory (ADL) database. During the image preprocessing stage, we applied two methods. The first method involved subtracting the mean of ImageNet images from all images. The second method involved subtracting the

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Handels, H., E. Rinast, Ch Busch, et al. "Image transfer and computer-supported cooperative diagnosis." Journal of Telemedicine and Telecare 3, no. 2 (1997): 103–7. http://dx.doi.org/10.1258/1357633971930940.

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The KAMEDIN system was designed as a low-cost communication tool as part of a computer-supported cooperative work project that included synchronized user interaction, telepointing and audioconferencing. During a five-month field trial, it was used for medical image transfer and cooperative diagnosis in 14 clinics and medical departments in Germany. During the field test, 297 teleconsultations were performed via ISDN and 875 MByte of data were transferred. An image compression ratio of 2-3 was obtained, so that the total quantity of data transferred corresponded to 14,000-21,000 magnetic resona

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Dheepan, G. M. Karpura, Shaik Mohammed Rafee, Prasanthi Badugu, and Sunil Kumar. "A DEEP LEARNING TECHNIQUE FOR EFFICIENT MULTIMEDIA FOR DATA COMPRESSION." ICTACT Journal on Image and Video Processing 14, no. 3 (2024): 3169–74. http://dx.doi.org/10.21917/ijivp.2024.0451.

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Medical image compression plays a pivotal role in efficient data storage and transmission, crucial for modern healthcare systems. This research proposes a convolutional transfer learning technique scheme tailored for multimedia data compression, specifically targeting medical images. In the background, the growing volume of medical imaging data and the demand for efficient storage and transmission underscore the need for innovative compression methods. Leveraging transfer learning from pre-trained convolutional neural networks (CNNs) designed for image recognition tasks, our methodology optimi

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Sheelavathy, Hamsavani R, Disha J, Bhavana C, and Bhoomika Rathod. "Image Steganography Technique based on Canny Edge Detection and Hamming Code for Medical Data." International Journal of Engineering and Advanced Technology 8, no. 5s (2019): 23–25. http://dx.doi.org/10.35940/ijeat.e1005.0585s19.

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Image steganography has major role in enhancing the confidentiality of sensitive information related to business information, research data, and health record data and so on. Here the sensitive data considered is Medical data. When the medical image is transmitted through in secure public network, there are chances for medical images to be tampered. To avoid intruders in viewing the sensitive data i.e. Medical information the need of hiding it becomes the foremost criteria. This project mainly aims at enhancing medical integrity. To achieve medical integrity, it is required to hide the medical

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Gu, Yi, and Qiankun Zheng. "A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images." Wireless Communications and Mobile Computing 2021 (April 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/9979606.

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Background. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C-means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribut

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Meng, Qingxin. "Exploration of hyperparameter efficiency for image style transfer." Applied and Computational Engineering 50, no. 1 (2024): 89–96. http://dx.doi.org/10.54254/2755-2721/50/20241240.

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Image style transfer is a popular computer vision technique that aims to merge the content of one image with the style of another to generate a unique, original image with a different aesthetic feel. Numerous models have been developed for various applications in this field, including portrait painting, art creation, and medical image processing, where additional information or annotations could be added to medical images, making them easier to read and understand. This study focuses on optimizing parameters within the pre-trained Visual Geometry Group (VGG19) network architecture, building on

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Zhang, Zhanhao. "The transferability of transfer learning model based on ImageNet for medical image classification tasks." Applied and Computational Engineering 18, no. 1 (2023): 143–51. http://dx.doi.org/10.54254/2755-2721/18/20230980.

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Transfer learning with pretrained weights is commonly based on the ImageNet dataset. However, ImageNet does not contain medical images, leaving the transferability of these pretrained weights for medical image classification an open question. The core purpose of this study is to investigate the impact of transfer learning on the accuracy of medical image classification, utilizing ResNet18, VGG11, AlexNet, and MobileNet, which are four of the most widely used neural network models. Specifically, this study aims to determine whether the incorporation of transfer learning techniques leads to sign

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M. Lupague, Ryan Marcus Jeremy, Romie C. Mabborang, Prof Alvin G. Bansil, and Melinda M. Lupague. "Assessing Transfer Learning Models for Medical Image Classification: A Comparative Study on Alzheimer’s MRI, Chest CT-Scan, and Chest X-ray Images." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (2023): 59–71. http://dx.doi.org/10.35940/ijrte.c7897.0912323.

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Deep learning has revolutionized the field of neural network models, offering limitless applications in various do- mains. This study focuses on Transfer Learning (TL), a technique leveraging pre-trained deep learning models trained on large datasets for image classification tasks. Specifically, this research explores the effectiveness of various transfer learning models in three medical image datasets: Alzheimer’s MRI images, Chest CT-Scan images, and Chest X-ray images. The main objective of this study is to assess and compare the performance of various TL models, including MobileNetV2, ResN

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Дисертації з теми "Medical Image Transfer"

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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Byers, Daniel James 1958. "Design of a high speed fiber optic network interface for medical image transfer." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276590.

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A high speed, 125 mega-bit per second data rate, data communication channel using fiber optic technology is described. Medical image data, generated by CT scanner or magnetic resonance imaging type imaging equipment, passes from standard American College of Radiology - National Electrical Manufactures Association (ACR-NEMA) interface equipment to the High Speed Fiber Optic Network Interface (HSFONI). The HSFONI implements the ACR-NEMA standard interface physical layer with fiber optics. The HSFONI accepts data from up to 8 devices and passes data to other devices or to a data base archive syst

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Prieto, Moreno Kernel Enrique. "Novel mathematical techniques for structural inversion and image reconstruction in medical imaging governed by a transport equation." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/novel-mathematical-techniques-for-structural-inversion-and-image-reconstruction-in-medical-imaging-governed-by-a-transport-equation(b45f5566-daa7-4d47-a982-cf479e360c6f).html.

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Since the inverse problem in Diffusive Optical Tomography (DOT) is nonlinear and severely ill-posed, only low resolution reconstructions are feasible when noise is added to the data nowadays. The purpose of this thesis is to improve image reconstruction in DOT of the main optical properties of tissues with some novel mathematical methods. We have used the Landweber (L) method, the Landweber-Kaczmarz (LK) method and its improved Loping-Landweber-Kaczmarz (L-LK) method combined with sparsity or with total variation regularizations for single and simultaneous image reconstructions of the absorpti

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Dickens, Erik. "Towards automatic detection and visualization of tissues in medical volume rendering." Thesis, Linköping University, Department of Science and Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9800.

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<p>The technique of volume rendering can be a powerful tool when visualizing 3D medical data sets. Its characteristic of capturing 3D internal structures within a 2D rendered image makes it attractive in the analysis. However, the applications that implement this technique fail to reach out to most of the supposed end-users at the clinics and radiology departments of today. This is primarily due to problems centered on the design of the Transfer Function (TF), the tool that makes tissues visually appear in the rendered image. The interaction with the TF is too complex for a supposed end-user a

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Saraiva, Luciano Albuquerque Lima. "Desenvolvimento de software para processamento de imagens quantitativas em ressonância magnética." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/17/17140/tde-21092006-143828/.

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O uso de análise quantitativa em radiologia médica tem sido de grande valia na detecção de alterações não acessíveis à análise visual simples, dita qualitativa, seja por serem muito sutis, seja por não estarem presentes nas técnicas de imagem de ressonância magnética convencional. Porém, certos tipos de quantificação exigem a aquisição softwares e de plataformas computacionais de alto custo, além de mão de obra especializada com conhecimento técnico em computação para operar em ambientes não intuitivos. Neste cenário o objetivo deste trabalho foi a implementação de um software para análise de

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Tee, Yee Kai. "Quantitative measurement of pH in stroke using chemical exchange saturation transfer magnetic resonance imaging." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:e5634676-55a5-43ef-92e7-12166f3d6bf0.

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Stroke is one of the leading causes of death and adult disability worldwide. The major therapeutic intervention for acute ischemic stroke is the administration of recombinant tissue plasminogen activator (rtPA) to help to restore blood flow to the brain. This has been shown to increase the survival rate and to reduce the disability of ischemic stroke patients. However, rtPA is associated with intracranial haemorrhage and thus its administration is currently limited to only about 5% of ischemic stroke patients. More advanced imaging techniques can be used to better stratify patients for rtPA tr

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Ma, Yanjun. "Medical Image Fusion Based on Wavelet Transform." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4245.

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Medical image is a core step of medical diagnosis and has been diffusely applied in modern medical domain. The technology of modern medical image is more and more mature which could present images in different modes and features. Medical image fusion is the technology that could compound two mutual images into one according to certain rules to achieve clear visual effect. By observing medical fusion image, doctor could easily confirm the position of illness. According to the mutual features of CT medical image and MRI medical image, based on the technology of wavelet transform, the paper prese

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Choi, Hyunjong. "Medical Image Registration Using Artificial Neural Network." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1523.

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Image registration is the transformation of different sets of images into one coordinate system in order to align and overlay multiple images. Image registration is used in many fields such as medical imaging, remote sensing, and computer vision. It is very important in medical research, where multiple images are acquired from different sensors at various points in time. This allows doctors to monitor the effects of treatments on patients in a certain region of interest over time. In this thesis, artificial neural networks with curvelet keypoints are used to estimate the parameters of registra

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Ibraheem, Mohammed Shaaban. "Logarithmic Discrete Wavelet Transform For High Quality Medical Image Compression." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066067/document.

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De nos jours, la compression de l'image médicale est un processus essentiel dans les systèmes de cybersanté. Compresser des images médicales de haute qualité est une exigence vitale pour éviter de mal diagnostiquer les examens médicaux par les radiologues. WAAVES est un algorithme de compression d'images médicales prometteur basé sur la transformée en ondelettes discrètes (DWT) qui permet d'obtenir une performance de compression élevée par rapport à l'état de la technique. Les principaux objectifs de ce travail sont d'améliorer la qualité d'image lors de la compression à l'aide de WAAVES et de

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Ibraheem, Mohammed Shaaban. "Logarithmic Discrete Wavelet Transform For High Quality Medical Image Compression." Electronic Thesis or Diss., Paris 6, 2017. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2017PA066067.pdf.

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Книги з теми "Medical Image Transfer"

Wang, Qian, Fausto Milletari, Hien V. Nguyen, et al., eds. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33391-1.

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Gotsi, Georgia, and Despina Provata, eds. Languages, Identities and Cultural Transfers. Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789462988071.

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What was the perception of Greece in Europe during the later nineteenth century, when the attraction of romantic philhellenism had waned? This volume focuses on the reception of medieval and modern Greece in the European press, rigorously analysing journals and newspapers published in England, France, Germany, Italy, and The Netherlands. The essays here suggest that reactions to the Greek state's progress and irredentist desires were followed among the European intelligentsia. Concurrently, new scholarship on the historical development of the Greek language and vernacular literature enhanced t

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Tang, Pao-chen. The Animist Imagination in East Asian Cinema. Amsterdam University Press, 2025. https://doi.org/10.5117/9789048563999.

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Whispering winds, speeding trains, wandering balloons, and swirling snowflakes—these are the living entities that humans find themselves enmeshed with in their ecological co-flourishing in contemporary East Asian cinema. Pao-chen Tang theorizes and analyzes this animist imagination—a new mode of filmmaking that delves into both the definition of the cinematic medium and how to live with the nonhuman. Moving images are animate beings and the animism of cinema further compels an eye-opening vision to examine East Asian history and ecological anxieties of our times. The shamanic protagonists of t

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Tsika, Noah. African Media in an Age of Extraction. Amsterdam University Press, 2025. https://doi.org/10.5117/9789048561254.

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African Media in an Age of Extraction takes a fresh, site-specific look at the relationship between moving images and the mining of natural resources, arguing that where we “place” Nollywood and other industries has important practical and conceptual consequences. Such locations are not just spatial metaphors but also tangible geographies with material connections to extractive economies. Sites of film production are often spaces of oil prospecting, timber harvesting, and mineral extraction—natural environments continuously transformed by capital. African Media in an Age of Extraction links su

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Zhou, Kevin, Qian Wang, M. Jorge Cardoso, et al. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First MICCAI Workshop, DART 2019, and ... Springer, 2019.

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Sayad, Cecilia. The Ghost in the Image. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190065768.001.0001.

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The Ghost in the Image offers a new take on the place that supernatural phenomena occupy in everyday life by examining the horror genre in fiction, documentary, and participative modes. The book covers a variety of media: spirit photography, ghost-hunting reality shows, documentary and fiction films based on the Amityville and Enfield hauntings, found-footage horror movies, experiential cinema, survival games, and creepypasta. These works transform our interest in ghosts into an interactive form of entertainment. Through a transmedial approach to horror, this book investigates our expectations

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Zacharasiewicz, Waldemar, and Siegfried Beer, eds. Cultural Politics, Transfer, and Propaganda. Mediated Narratives and Images in Austrian-American Relations. Verlag der Österreichischen Akademie der Wissenschaften, 2021. http://dx.doi.org/10.1553/978oeaw88742.

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The interdisciplinary collection contains 16 essays by scholars from literary and cultural studies, by sociologists, historians, musicologists, art historians and media experts. Following the introduction to the key issues in cultural politics and propaganda and a synopsis of the essays, an article surveys the reciprocal perception of Austria and the USA from the 18th century onwards. The following essays analyze various historical phases in the complex relationship between Austria (and Central Europe) and the USA. Several essays survey the strategies used to promote Austrian tourism and contr

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Marciniak-Kajzer, Anna. Archaeology on Medieval Knights’ Manor Houses in Poland. Wydawnictwo Uniwersytetu Łódzkiego, 2016. http://dx.doi.org/10.18778/8088-002-3.

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The relicts of medieval knights’ manor houses in Poland today are so called “grodziska stożkowate” (motte) - the anonymous hills having in themselves remnants of wooden buildings, exceptionally made of stone or brick and numerous tiny artifacts being the trace of the past household equipment. Unlike to the castles they are not so often visited but more often destroyed. The book presents the image of medieval knights’ manor houses, which we know due to archaeological excavations carried on for half a century. Description of buildings household equipment and movables used by the people of the pa

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Starks, Tricia. Smoking under the Tsars. Cornell University Press, 2018. http://dx.doi.org/10.7591/cornell/9781501722059.001.0001.

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Using unusual sources and approaching tobacco from the perspective of users, producers, and objectors, this monograph provides an unparalleled view of the early transfer by the Russian market to smoking and presents the addictive, nicotine-soaked Russian cigarette – the papirosa -- and the sensory, medical, social, cultural, and gendered consequences of this unique style of tobacco use. Starting with the papirosa’s introduction in the nineteenth century and foundation as a cultural and imperial construct, the monograph moves through its emergence as a mass-use product of revolutionary potentia

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Anger, Jiří. Towards a Film Theory from Below. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9798765107300.

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Operating between film theory, media philosophy, archival practice, and audiovisual research, Jirí Anger focuses on the relationship between figuration and materiality in early films, experimental found footage cinema, and video essays. Would it be possible to do film theory from below, through the perspective of moving-image objects, of their multifarious details and facets, however marginal, unintentional, or aleatory they might be? Could we treat scratches, stains, and shakes in archival footage as speculatively and aesthetically generative features? Do these material actors have the capaci

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Частини книг з теми "Medical Image Transfer"

Toennies, Klaus D. "Image Storage and Transfer." In Guide to Medical Image Analysis. Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7320-5_3.

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Toennies, Klaus D. "Image Storage and Transfer." In Guide to Medical Image Analysis. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2751-2_3.

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Tanno, Ryutaro, Aurobrata Ghosh, Francesco Grussu, Enrico Kaden, Antonio Criminisi, and Daniel C. Alexander. "Bayesian Image Quality Transfer." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46723-8_31.

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Bao, Yiming, Jun Wang, Tong Li, et al. "Self-adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images." In Ophthalmic Medical Image Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87000-3_14.

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Forsberg, Daniel, Claes Lundström, Mats Andersson, and Hans Knutsson. "Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes." In Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_55.

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Xiao, Junfei, Lequan Yu, Zongwei Zhou, et al. "CateNorm: Categorical Normalization for Robust Medical Image Segmentation." In Domain Adaptation and Representation Transfer. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16852-9_13.

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Ma, DongAo, Mohammad Reza Hosseinzadeh Taher, Jiaxuan Pang, et al. "Benchmarking and Boosting Transformers for Medical Image Classification." In Domain Adaptation and Representation Transfer. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16852-9_2.

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Radhika Shetty, D. S., and P. J. Antony. "Transfer Learning Techniques in Medical Image Classification." In ICT: Smart Systems and Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9489-2_21.

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Cai, Shaojin, Yuyang Xue, Qinquan Gao, et al. "Stain Style Transfer Using Transitive Adversarial Networks." In Machine Learning for Medical Image Reconstruction. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33843-5_15.

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Andreeva, Rayna, Alessandro Fontanella, Ylenia Giarratano, and Miguel O. Bernabeu. "DR Detection Using Optical Coherence Tomography Angiography (OCTA): A Transfer Learning Approach with Robustness Analysis." In Ophthalmic Medical Image Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63419-3_2.

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Тези доповідей конференцій з теми "Medical Image Transfer"

Lu, Yucheng, Dovile Juodelyte, Jonathan D. Victor, and Veronika Cheplygina. "Exploring connections of spectral analysis and transfer learning in medical imaging." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047670.

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Tyagi, Priya, Avadhesh Kumar Sharma, Palak Masson, Manika Manwal, Pawan Kumar Goel, and Neeraj Varshney. "Leveraging Transfer Learning for Robust Medical Image Segmentation." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911792.

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Korkmaz, İlker, and Fatih Soygazi. "Gastrointestinal Image Classification Using Deep Learning Architectures via Transfer Learning." In 2024 Medical Technologies Congress (TIPTEKNO). IEEE, 2024. http://dx.doi.org/10.1109/tiptekno63488.2024.10755310.

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Singh, Maninder, Vipin Kr Kushwaha, Shiva Gupta, and B. Sharan. "Deep Learning-Driven Knowledge Transfer for Automated Medical Image Segmentation." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986641.

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Lonia, Giovanni, Davide Ciraolo, Maria Fazio, Massimo Villari, and Antonio Celesti. "Comparing CNNs and ViTs for Medical Image Classification Leveraging Transfer Learning." In 2024 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2024. http://dx.doi.org/10.1109/iscc61673.2024.10733732.

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Yang, Jingyun, Guoqing Zhang, Jingge Wang, and Yang Li. "Graph-guided Source Selection with Sequential Transfer for Medical Image Segmentation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821855.

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Malla, Prince Priya, and Sudhakar Sahu. "A Comparative Study of Transfer Learning Approaches For Medical Image Analysis." In 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2024. https://doi.org/10.1109/pdgc64653.2024.10984272.

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Rautaray, Gadadhar, Dayal Kumar Behera, Sanjeev Kumar Dash, Jiten Kumar Mohanty, Ranjit Kumar Behera, and Mahendra Kumar Gourisaria. "Performance Comparison of Transfer Learning Models for X-ray Medical Image Classification." In 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA). IEEE, 2024. https://doi.org/10.1109/icscsa64454.2024.00016.

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J.Logeshwaran, N. Yuvaraj, Vikrant Sharma, Rishi Prakash Shukla, and Divya Kumar. "A Meta Learning Approach for Improving Medical Image Segmentation with Transfer Learning." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10922056.

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Alam, Md Zehan, Tonmoy Roy, H. M. Nahid Kawsar, and Iffat Rimi. "Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022326.

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Звіти організацій з теми "Medical Image Transfer"

Hammouti, A., S. Larmagnat, C. Rivard, and D. Pham Van Bang. Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331502.

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Анотація:

Non-intrusive techniques such as medical CT-Scan or micro-CT allow the definition of 3D connected pore networks in porous materials, such as sedimentary rocks or concrete. The definition of these networks is a key step towards the evaluation of fluid flow and heat transfer in energy resource (e.g., hydrocarbon and geothermal reservoirs) and CO2 sequestration research projects. As material heterogeneities play a role at all scales (from micro- to project-scale), numerical models represent a powerful tool for bridging the gap between small-scale measurements provided by X-ray imaging techniques

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