Zobrazeno 1 - 10
of 1 786
pro vyhledávání: '"Petersen, P E"'
Autor:
Huang, Chengwu, Lok, U-Wai, Zhang, Jingke, Zhu, Xiang Yang, Krier, James D., Stern, Amy, Knoll, Kate M., Petersen, Kendra E., Robinson, Kathryn A., Hesley, Gina K., Bentall, Andrew J., Atwell, Thomas D., Rule, Andrew D., Lerman, Lilach O., Chen, Shigao
Ultrasound localization microscopy (ULM) enables microvascular imaging at spatial resolutions beyond the acoustic diffraction limit, offering significant clinical potentials. However, ULM performance relies heavily on microbubble (MB) signal sparsity
Externí odkaz:
http://arxiv.org/abs/2412.18077
Autor:
Skorupko, Grzegorz, Osuala, Richard, Szafranowska, Zuzanna, Kushibar, Kaisar, Aung, Nay, Petersen, Steffen E, Lekadir, Karim, Gkontra, Polyxeni
The progress in deep learning solutions for disease diagnosis and prognosis based on cardiac magnetic resonance imaging is hindered by highly imbalanced and biased training data. To address this issue, we propose a method to alleviate imbalances inhe
Externí odkaz:
http://arxiv.org/abs/2403.19508
Autor:
Olšteins, Dāgs, Nagda, Gunjan, Carrad, Damon J., Beznasyuk, Daria V., Petersen, Christian E. N., Martí-Sánchez, Sara, Arbiol, Jordi, Jespersen, Thomas Sand
New approaches such as selective area growth, where crystal growth is lithographically controlled, allow the integration of bottom-up grown semiconductor nanomaterials in large-scale classical and quantum nanoelectronics. This calls for assessment an
Externí odkaz:
http://arxiv.org/abs/2401.05084
Autor:
Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, Starmans, Martijn P A
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinica
Externí odkaz:
http://arxiv.org/abs/2309.12325
Autor:
Salih, Ahmed, Raisi-Estabragh, Zahra, Galazzo, Ilaria Boscolo, Radeva, Petia, Petersen, Steffen E., Menegaz, Gloria, Lekadir, Karim
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model works with the aim of making ML models more transpare
Externí odkaz:
http://arxiv.org/abs/2305.02012
Autor:
Olšteins, Dāgs, Nagda, Gunjan, Carrad, Damon J., Beznasiuk, Daria V., Petersen, Christian E. N., Martí-Sánchez, Sara, Arbiol, Jordi, Jespersen, Thomas Sand
Bottom-up grown nanomaterials play an integral role in the development of quantum technologies. Among these, semiconductor nanowires (NWs) are widely used in proof-of-principle experiments, however, difficulties in parallel processing of conventional
Externí odkaz:
http://arxiv.org/abs/2304.12765
Autor:
Salih, Ahmed, Galazzo, Ilaria Boscolo, Raisi-Estabragh, Zahra, Petersen, Steffen E., Menegaz, Gloria, Radeva, Petia
Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpretability of models and makes the models more trustworthy and transparen
Externí odkaz:
http://arxiv.org/abs/2304.01717
Autor:
Mariscal-Harana, Jorge, Asher, Clint, Vergani, Vittoria, Rizvi, Maleeha, Keehn, Louise, Kim, Raymond J., Judd, Robert M., Petersen, Steffen E., Razavi, Reza, King, Andrew, Ruijsink, Bram, Puyol-Antón, Esther
Artificial intelligence (AI) techniques have been proposed for automating analysis of short axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We deve
Externí odkaz:
http://arxiv.org/abs/2206.08137
Autor:
Carrad, Damon J., Stampfer, Lukas, Olsteins, Dags, Petersen, Christian E. N., Khan, Sabbir A., Krogstrup, Peter, Jespersen, Thomas Sand
Semiconductor/superconductor hybrids exhibit a range of phenomena that can be exploited for the study of novel physics and the development of new technologies. Understanding the origin the energy spectrum of such hybrids is therefore a crucial goal.
Externí odkaz:
http://arxiv.org/abs/2205.03217
Autor:
Petersen, Mark E., Beard, Randal W.
The Integrated Probabilistic Data Association Filter (IPDAF) is a target tracking algorithm based on the Probabilistic Data Association Filter that calculates a statistical measure that indicates if an estimated representation of the target properly
Externí odkaz:
http://arxiv.org/abs/2108.07265