Literatura académica sobre el tema "U-NET CNN"
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Artículos de revistas sobre el tema "U-NET CNN"
Sariturk, Batuhan y Dursun Zafer Seker. "A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images". Sensors 22, n.º 19 (8 de octubre de 2022): 7624. http://dx.doi.org/10.3390/s22197624.
Texto completoChoi, Keong-Hun y Jong-Eun Ha. "Edge Detection based-on U-Net using Edge Classification CNN". Journal of Institute of Control, Robotics and Systems 25, n.º 8 (31 de agosto de 2019): 684–89. http://dx.doi.org/10.5302/j.icros.2019.19.0119.
Texto completoDi Benedetto, Alessandro, Margherita Fiani y Lucas Matias Gujski. "U-Net-Based CNN Architecture for Road Crack Segmentation". Infrastructures 8, n.º 5 (6 de mayo de 2023): 90. http://dx.doi.org/10.3390/infrastructures8050090.
Texto completoDjohar, Muhammad Awaludin, Anita Desiani, Dewi Lestari Dwi Putri, Des Alwine Zayanti, Ali Amran, Irmeilyana Irmeilyana y Novi Rustiana Dewi. "Segmentasi Citra Hati Menggunakan Metode Convolutional Neural Network dengan Arsitektur U-Net". JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, n.º 1 (23 de julio de 2022): 221–34. http://dx.doi.org/10.31289/jite.v6i1.6751.
Texto completoMiron, Casian, Laura Ioana Grigoras, Radu Ciucu y Vasile Manta. "Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture". Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section 67, n.º 2 (1 de junio de 2021): 41–52. http://dx.doi.org/10.2478/bipie-2021-0010.
Texto completoSariturk, Batuhan, Damla Kumbasar y Dursun Zafer Seker. "Comparative Analysis of Different CNN Models for Building Segmentation from Satellite and UAV Images". Photogrammetric Engineering & Remote Sensing 89, n.º 2 (1 de febrero de 2023): 97–105. http://dx.doi.org/10.14358/pers.22-00084r2.
Texto completoErdem, Firat, Nuri Erkin Ocer, Dilek Kucuk Matci, Gordana Kaplan y Ugur Avdan. "Apricot Tree Detection from UAV-Images Using Mask R-CNN and U-Net". Photogrammetric Engineering & Remote Sensing 89, n.º 2 (1 de febrero de 2023): 89–96. http://dx.doi.org/10.14358/pers.22-00086r2.
Texto completoK.Narasimha Rao, Kesani Prudhvidhar Reddy, Gopavarapu Sai Satya Sreekar y Gade Gopinath Reddy. "Retinal blood vessels segmentation using CNN algorithm". international journal of engineering technology and management sciences 7, n.º 3 (2023): 499–504. http://dx.doi.org/10.46647/ijetms.2023.v07i03.70.
Texto completoLutsenko, V. S. y A. E. Shukhman. "SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS". Vestnik komp'iuternykh i informatsionnykh tekhnologii, n.º 216 (junio de 2022): 40–50. http://dx.doi.org/10.14489/vkit.2022.06.pp.040-050.
Texto completoYounisse, Remah, Rawan Ghnemat y Jaafer Al Saraireh. "Fine-tuning U-net for medical image segmentation based on activation function, optimizer and pooling layer". International Journal of Electrical and Computer Engineering (IJECE) 13, n.º 5 (1 de octubre de 2023): 5406. http://dx.doi.org/10.11591/ijece.v13i5.pp5406-5417.
Texto completoTesis sobre el tema "U-NET CNN"
Scotti, Alessandro. "Sviluppo e validazione di un nuovo approccio basato su reti neurali convoluzionali 3D per la valutazione della progressione della malattia policistica renale autosomica dominante". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Buscar texto completoHellgren, Robin y Martin Axelsson. "An evaluation of using a U-Net CNN with a random forest pre-screener : On a dataset of hand-drawn maps provided by länsstyrelsen i Jönköping". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20003.
Texto completoVincenzi, Fabian. "Reti neurali convoluzionali per il miglioramento di immagini tomografiche ad angoli limitati". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/22199/.
Texto completoBerezina, Polina. "Enhancing Hurricane Damage Assessment from Satellite Images Using Deep Learning". The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587554383454681.
Texto completoDíaz, Pinto Andrés Yesid. "Machine Learning for Glaucoma Assessment using Fundus Images". Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/124351.
Texto completo[CAT] Les imatges de fons d'ull són molt utilitzades pels oftalmòlegs per a l'avaluació de la retina i la detecció de glaucoma. Aquesta patologia és la segona causa de ceguesa al món, segons estudis de l'Organització Mundial de la Salut (OMS). En aquesta tesi doctoral, s'estudien algoritmes d'aprenentatge automàtic (machine learning) per a l'avaluació automàtica del glaucoma usant imatges de fons d'ull. En primer lloc, es proposen dos mètodes per a la segmentació automàtica. El primer mètode utilitza la transformació Watershed Estocàstica per segmentar la copa òptica i després mesurar característiques clíniques com la relació Copa / Disc i la regla ISNT. El segon mètode és una arquitectura U-Net que s'usa específicament per a la segmentació del disc òptic i la copa òptica. A continuació, es presenten sistemes automàtics d'avaluació del glaucoma basats en xarxes neuronals convolucionals (CNN per les sigles en anglès). En aquest enfocament s'utilitzen diferents models entrenats en ImageNet com classificadors automàtics de glaucoma, usant fine-tuning. Aquesta nova tècnica permet detectar el glaucoma sense segmentació prèvia o extracció de característiques. A més, aquest enfocament presenta una millora considerable del rendiment comparat amb altres treballs de l'estat de l'art. En tercer lloc, donada la dificultat d'obtenir grans quantitats d'imatges etiquetades (glaucoma / no glaucoma), aquesta tesi també aborda el problema de la síntesi d'imatges de la retina. En concret es van analitzar dues arquitectures diferents per a la síntesi d'imatges, les arquitectures Variational Autoencoder (VAE) i la Generative adversarial Networks (GAN). Amb aquestes arquitectures es van generar imatges sintètiques que es van analitzar qualitativament i quantitativament, obtenint un rendiment similar a altres treballs a la literatura. Finalment, en aquesta tesi es planteja la utilització d'un tipus de GAN (DCGAN) com a alternativa als sistemes automàtics d'avaluació del glaucoma presentats anteriorment. Per assolir aquest objectiu es va implementar un algoritme d'aprenentatge semi-supervisat.
[EN] Fundus images are widely used by ophthalmologists to assess the retina and detect glaucoma, which is, according to studies from the World Health Organization (WHO), the second cause of blindness worldwide. In this thesis, machine learning algorithms for automatic glaucoma assessment using fundus images are studied. First, two methods for automatic segmentation are proposed. The first method uses the Stochastic Watershed transformation to segment the optic cup and measures clinical features such as the Cup/Disc ratio and ISNT rule. The second method is a U-Net architecture focused on the optic disc and optic cup segmentation task. Secondly, automated glaucoma assessment systems using convolutional neural networks (CNNs) are presented. In this approach, different ImageNet-trained models are fine-tuned and used as automatic glaucoma classifiers. These new techniques allow detecting glaucoma without previous segmentation or feature extraction. Moreover, it improves the performance of other state-of-art works. Thirdly, given the difficulty of getting large amounts of glaucoma-labelled images, this thesis addresses the problem of retinal image synthesis. Two different architectures for image synthesis, the Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) architectures, were analysed. Using these models, synthetic images that were qualitative and quantitative analysed, reporting state-of-the-art performance, were generated. Finally, an adversarial model is used to create an alternative automatic glaucoma assessment system. In this part, a semi-supervised learning algorithm was implemented to reach this goal.
The research derived from this doctoral thesis has been supported by the Generalitat Valenciana under the scholarship Santiago Grisolía [GRISOLIA/2015/027].
Díaz Pinto, AY. (2019). Machine Learning for Glaucoma Assessment using Fundus Images [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/124351
TESIS
Sousa, Joana Vale Amaro de. "Lung Segmentation in CT Images: A CNN U-Net hybrid approach on a cross-cohort dataset". Master's thesis, 2021. https://hdl.handle.net/10216/137790.
Texto completoLibros sobre el tema "U-NET CNN"
Tse, Peter U. Two Types of Libertarian Free Will Are Realized in the Human Brain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190460723.003.0010.
Texto completoBiałowąs, Sylwester, ed. Experimental design and biometric research. Toward innovations. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, 2021. http://dx.doi.org/10.18559/978-83-8211-079-1.
Texto completoSkupio, Rafał. Zastosowanie nieinwazyjnych pomiarów rdzeni wiertniczych do zwiększenia informacji na temat parametrów skał zbiornikowych. Instytut Nafty i Gazu - Państwowy Instytut Badawczy, 2022. http://dx.doi.org/10.18668/pn2022.237.
Texto completoLos ancestrales juegos y deportes de pelota maya en Mesoamérica contemporánea. Universidad Libre sede principal, 2022. http://dx.doi.org/10.18041/978-628-7580-08-4.
Texto completoCapítulos de libros sobre el tema "U-NET CNN"
Kumaravelan, Umashankar y M. Nivedita. "Localized Super Resolution for Foreground Images Using U-Net and MR-CNN". En Lecture Notes in Electrical Engineering, 25–39. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7169-3_3.
Texto completoSharma, Utkarsh, Nimish Nigam, Ujjawal Kumar, Vinay Kumar, Sadanand Yadav, Ashish Pandey y Rakesh Kumar Singh. "Abnormality Detection in Heart Using Combination of CNN, RNN and U-Net". En VLSI, Communication and Signal Processing, 135–46. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0973-5_10.
Texto completoKonopczyński, Tomasz, Ron Heiman, Piotr Woźnicki, Paweł Gniewek, Marie-Cécilia Duvernoy, Oskar Hallatschek y Jürgen Hesser. "Instance Segmentation of Densely Packed Cells Using a Hybrid Model of U-Net and Mask R-CNN". En Artificial Intelligence and Soft Computing, 626–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61401-0_58.
Texto completoMaqsood, Sarmad, Robertas Damasevicius y Faisal Mehmood Shah. "An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification". En Computational Science and Its Applications – ICCSA 2021, 105–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86976-2_8.
Texto completoChelle-Michou, Cyril y Urs Schaltegger. "U–Pb Dating of Mineral Deposits: From Age Constraints to Ore-Forming Processes". En Isotopes in Economic Geology, Metallogenesis and Exploration, 37–87. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27897-6_3.
Texto completoZhang, Jindan, Jun Cai, Ying Su, Qingyou He y Xinyue Lin. "Research and Development and Pilot Application of Innovative Technology of Prefabricated Concrete". En Lecture Notes in Civil Engineering, 226–37. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1260-3_20.
Texto completoHogan, Ciarán y Ganesh Sistu. "Automatic Vehicle Ego Body Extraction for Reducing False Detections in Automated Driving Applications". En Communications in Computer and Information Science, 264–75. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_21.
Texto completoSikdar, Debosmita, Ivy Kanungo y Dipanwita Das. "Microbial Enzymes: A Summary Focusing on Biotechnology Prospective for Combating Industrial Pollutants". En Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 70–76. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_8.
Texto completoYildirim, Kemal, Sami Al-Nawaiseh, Sophia Ehlers, Lukas Schießer, Michael Storck, Tobias Brix, Nicole Eter y Julian Varghese. "U-Net-Based Segmentation of Current Imaging Biomarkers in OCT-Scans of Patients with Age Related Macular Degeneration". En Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230315.
Texto completoIslam, Mohammad Tariqul, Ferdaus Ahmed, Mowafa Househ y Tanvir Alam. "Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning". En Studies in Health Technology and Informatics. IOS Press, 2023. http://dx.doi.org/10.3233/shti230576.
Texto completoActas de conferencias sobre el tema "U-NET CNN"
Zhang, Chengzhu y Yuxiang Xing. "CT artifact reduction via U-net CNN". En Image Processing, editado por Elsa D. Angelini y Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293903.
Texto completoGhanshala, Anshul, Aakarshan Chauhan, Manoj Diwakar y Sachin Sharma. "Brain Tumor Detection Using U-Net and 3D CNN Architecture". En 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2022. http://dx.doi.org/10.1109/icccis56430.2022.10037660.
Texto completoLiu, Yang y Wei Yang. "Automatic liver segmentation using U-net in the assistance of CNN". En 2020 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE, 2020. http://dx.doi.org/10.1109/icicas51530.2020.00083.
Texto completoSuman, Abdulla Al, Yash Khemchandani, Md Asikuzzaman, Alexandra Louise Webb, Diana M. Perriman, Murat Tahtali y Mark R. Pickering. "Evaluation of U-Net CNN Approaches for Human Neck MRI Segmentation". En 2020 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2020. http://dx.doi.org/10.1109/dicta51227.2020.9363385.
Texto completoHsu, Aaron W. y Rodrigo Girão Serrão. "U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning". En ARRAY '23: 9th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3589246.3595371.
Texto completoSushma, B., C. K. Raghavendra y J. Prashanth. "CNN based U-Net with Modified Skip Connections for Colon Polyp Segmentation". En 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021. http://dx.doi.org/10.1109/iccmc51019.2021.9418037.
Texto completoLi, Yuqin, Zhengang Jiang, Ke Zhang, Weili Shi, Fei He y Jianhua Liu. "Dense-U-Net: A novel densely connected CNN for lung fields segmentation". En 2020 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2020. http://dx.doi.org/10.1109/icvrv51359.2020.00035.
Texto completoNour, Abdala, Sherif Saad y Boubakeur Boufama. "Prostate biomedical images segmentation and classification by using U-NET CNN model". En BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459930.3471169.
Texto completoWang, Yinglong y Lyu Zhou. "A Lung Nodule Detector Based on U-Net and 3D-CNN Model". En 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI). IEEE, 2021. http://dx.doi.org/10.1109/cei52496.2021.9574604.
Texto completoHAN, GUILAI, WEI LIU, BENGUO YU, XIAOLING LI, LU LIU y HAIXIA LI. "The Detection and Recognition of Pulmonary Nodules Based on U-net and CNN". En CSAI 2020: 2020 4th International Conference on Computer Science and Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3445815.3445838.
Texto completoInformes sobre el tema "U-NET CNN"
Mevarech, Moshe, Jeremy Bruenn y Yigal Koltin. Virus Encoded Toxin of the Corn Smut Ustilago Maydis - Isolation of Receptors and Mapping Functional Domains. United States Department of Agriculture, septiembre de 1995. http://dx.doi.org/10.32747/1995.7613022.bard.
Texto completoDownard, Alicia, Stephen Semmens y Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), abril de 2021. http://dx.doi.org/10.21079/11681/40439.
Texto completoReine, Kevin. A literature review of beach nourishment impacts on marine turtles. Engineer Research and Development Center (U.S.), marzo de 2022. http://dx.doi.org/10.21079/11681/43829.
Texto completoPulugurtha, Srinivas S., Abimbola Ogungbire y Chirag Akbari. Modeling and Evaluating Alternatives to Enhance Access to an Airport and Meet Future Expansion Needs. Mineta Transportation Institute, abril de 2023. http://dx.doi.org/10.31979/mti.2023.2120.
Texto completoTaylor. L51755 Development and Testing of an Advanced Technology Vibration Transmission. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), julio de 1996. http://dx.doi.org/10.55274/r0010124.
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