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Artykuły w czasopismach na temat "BREAKHIS DATASET"
Joshi, Shubhangi A., Anupkumar M. Bongale, P. Olof Olsson, Siddhaling Urolagin, Deepak Dharrao i Arunkumar Bongale. "Enhanced Pre-Trained Xception Model Transfer Learned for Breast Cancer Detection". Computation 11, nr 3 (13.03.2023): 59. http://dx.doi.org/10.3390/computation11030059.
Pełny tekst źródłaXu, Xuebin, Meijuan An, Jiada Zhang, Wei Liu i Longbin Lu. "A High-Precision Classification Method of Mammary Cancer Based on Improved DenseNet Driven by an Attention Mechanism". Computational and Mathematical Methods in Medicine 2022 (14.05.2022): 1–14. http://dx.doi.org/10.1155/2022/8585036.
Pełny tekst źródłaOgundokun, Roseline Oluwaseun, Sanjay Misra, Akinyemi Omololu Akinrotimi i Hasan Ogul. "MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors". Sensors 23, nr 2 (6.01.2023): 656. http://dx.doi.org/10.3390/s23020656.
Pełny tekst źródłaUkwuoma, Chiagoziem C., Md Altab Hossain, Jehoiada K. Jackson, Grace U. Nneji, Happy N. Monday i Zhiguang Qin. "Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head". Diagnostics 12, nr 5 (5.05.2022): 1152. http://dx.doi.org/10.3390/diagnostics12051152.
Pełny tekst źródłaMohanakurup, Vinodkumar, Syam Machinathu Parambil Gangadharan, Pallavi Goel, Devvret Verma, Sameer Alshehri, Ramgopal Kashyap i Baitullah Malakhil. "Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network". Computational Intelligence and Neuroscience 2022 (6.07.2022): 1–10. http://dx.doi.org/10.1155/2022/8517706.
Pełny tekst źródłaNahid, Abdullah-Al, Mohamad Ali Mehrabi i Yinan Kong. "Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering". BioMed Research International 2018 (2018): 1–20. http://dx.doi.org/10.1155/2018/2362108.
Pełny tekst źródłaSun, Yixin, Lei Wu, Peng Chen, Feng Zhang i Lifeng Xu. "Using deep learning in pathology image analysis: A novel active learning strategy based on latent representation". Electronic Research Archive 31, nr 9 (2023): 5340–61. http://dx.doi.org/10.3934/era.2023271.
Pełny tekst źródłaIstighosah, Maie, Andi Sunyoto i Tonny Hidayat. "Breast Cancer Detection in Histopathology Images using ResNet101 Architecture". sinkron 8, nr 4 (1.10.2023): 2138–49. http://dx.doi.org/10.33395/sinkron.v8i4.12948.
Pełny tekst źródłaLi, Lingxiao, Niantao Xie i Sha Yuan. "A Federated Learning Framework for Breast Cancer Histopathological Image Classification". Electronics 11, nr 22 (16.11.2022): 3767. http://dx.doi.org/10.3390/electronics11223767.
Pełny tekst źródłaBurrai, Giovanni P., Andrea Gabrieli, Marta Polinas, Claudio Murgia, Maria Paola Becchere, Pierfranco Demontis i Elisabetta Antuofermo. "Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis". Animals 13, nr 9 (6.05.2023): 1563. http://dx.doi.org/10.3390/ani13091563.
Pełny tekst źródłaRozprawy doktorskie na temat "BREAKHIS DATASET"
Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units". ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.
Pełny tekst źródłaSAADIZADEH, SAMAN. "SIGNIFICANTLY ACCURATE SYSTEM FOR BREAST CANCER MALIGNANCY OR BENIGN CLASSIFICATION". Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19429.
Pełny tekst źródłaKsiążki na temat "BREAKHIS DATASET"
Tan, Yeling. Disaggregating China, Inc. Cornell University Press, 2021. http://dx.doi.org/10.7591/cornell/9781501759635.001.0001.
Pełny tekst źródłaLyall, Jason. Divided Armies. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691192444.001.0001.
Pełny tekst źródłaCzęści książek na temat "BREAKHIS DATASET"
Agarwal, Pinky, Anju Yadav i Pratistha Mathur. "Breast Cancer Prediction on BreakHis Dataset Using Deep CNN and Transfer Learning Model". W Data Engineering for Smart Systems, 77–88. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2641-8_8.
Pełny tekst źródłaSchirmer, Pascal A., i Iosif Mporas. "Binary versus Multiclass Deep Learning Modelling in Energy Disaggregation". W Springer Proceedings in Energy, 45–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_6.
Pełny tekst źródłaDellmuth, Lisa. "EU Spending Effects on Regional Well-Being". W Is Europe Good for You?, 77–98. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529217469.003.0005.
Pełny tekst źródłaThomas, D. J., B. D. Sutton, J. W. Ferguson i E. Price. "Spatially Resolved Detonation Pressure Data From Rate Sticks". W Future Developments in Explosives and Energetics, 105–19. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781788017855-00105.
Pełny tekst źródłaThomas, D. J., B. D. Sutton, J. W. Ferguson i E. Price. "Spatially Resolved Detonation Pressure Data From Rate Sticks". W Future Developments in Explosives and Energetics, 105–19. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781839162350-00105.
Pełny tekst źródłaStreszczenia konferencji na temat "BREAKHIS DATASET"
MAYOUF, MOUNA SABRINE, i FLORENCE DUPIN DE SAINT-Cyr. "Curriculum Incremental Deep Learning on BreakHis DataSet". W ICCTA 2022: 2022 8th International Conference on Computer Technology Applications. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3543712.3543747.
Pełny tekst źródłaSantos, Stefane A., Andressa G. Moreira i Ialis C. P. Junior. "Análise comparativa da influência de otimizadores no desempenho de uma CNN para detecção do câncer de mama". W Escola Regional de Computação Ceará, Maranhão, Piauí. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/ercemapi.2021.17901.
Pełny tekst źródłaFreitas, Mario Pinto, Marcos Gabriel Mendes Lauande, Geraldo Braz Júnior, Marcus Vinicius Oliveira, Gabriel Costa, Matheus Levy, Anselmo Cardoso de Paiva i João D. Sousa de Almeida. "Aplicando MultiInstance Learning (MIL) para o Diagnóstico de Câncer de Mama em Imagens Histopatológicas". W Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbcas.2022.222673.
Pełny tekst źródłaSantos, Marta C., Ana I. Borges, Davide R. Carneiro i Flora J. Ferreira. "Synthetic dataset to study breaks in the consumer’s water consumption patterns". W ICoMS 2021: 2021 4th International Conference on Mathematics and Statistics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3475827.3475836.
Pełny tekst źródłaPal, S., C. Iek, L. J. Peltier, A. Smirnov, K. J. Knight, D. Zheng i J. Jarvis. "Verification and Validation of CFD Model to Predict Jet Loads and Blast Wave Pressures From High Pressure Superheated Steam Line Break". W ASME 2016 Power Conference collocated with the ASME 2016 10th International Conference on Energy Sustainability and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/power2016-59675.
Pełny tekst źródłaLamb, Nikolas, Cameron Palmer, Benjamin Molloy, Sean Banerjee i Natasha Kholgade Banerjee. "Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts". W 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.00454.
Pełny tekst źródłaPazi, Idan, Dvir Ginzburg i Dan Raviv. "Unsupervised Scale-Invariant Multispectral Shape Matching". W 24th Irish Machine Vision and Image Processing Conference. Irish Pattern Recognition and Classification Society, 2022. http://dx.doi.org/10.56541/vhmq4826.
Pełny tekst źródłaHan, Jiyeon, Kyowoon Lee, Anh Tong i Jaesik Choi. "Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/340.
Pełny tekst źródłaTolstaya, E., A. Shakirov i M. Mezghani. "Lithology Prediction from Drill Cutting Images Using Convolutional Neural Networks and Automated Dataset Cleaning". W ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216418-ms.
Pełny tekst źródłaLi, Boyang, Yurong Cheng, Ye Yuan, Guoren Wang i Lei Chen. "Simultaneous Arrival Matching for New Spatial Crowdsourcing Platforms". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/178.
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