Zobrazeno 1 - 10
of 476
pro vyhledávání: '"Zuluaga, Maria"'
Combining multiple modalities carrying complementary information through multimodal learning (MML) has shown considerable benefits for diagnosing multiple pathologies. However, the robustness of multimodal models to missing modalities is often overlo
Externí odkaz:
http://arxiv.org/abs/2407.20768
Autor:
Yang, Kaiyuan, Musio, Fabio, Ma, Yihui, Juchler, Norman, Paetzold, Johannes C., Al-Maskari, Rami, Höher, Luciano, Li, Hongwei Bran, Hamamci, Ibrahim Ethem, Sekuboyina, Anjany, Shit, Suprosanna, Huang, Houjing, Prabhakar, Chinmay, de la Rosa, Ezequiel, Waldmannstetter, Diana, Kofler, Florian, Navarro, Fernando, Menten, Martin, Ezhov, Ivan, Rueckert, Daniel, Vos, Iris, Ruigrok, Ynte, Velthuis, Birgitta, Kuijf, Hugo, Hämmerli, Julien, Wurster, Catherine, Bijlenga, Philippe, Westphal, Laura, Bisschop, Jeroen, Colombo, Elisa, Baazaoui, Hakim, Makmur, Andrew, Hallinan, James, Wiestler, Bene, Kirschke, Jan S., Wiest, Roland, Montagnon, Emmanuel, Letourneau-Guillon, Laurent, Galdran, Adrian, Galati, Francesco, Falcetta, Daniele, Zuluaga, Maria A., Lin, Chaolong, Zhao, Haoran, Zhang, Zehan, Ra, Sinyoung, Hwang, Jongyun, Park, Hyunjin, Chen, Junqiang, Wodzinski, Marek, Müller, Henning, Shi, Pengcheng, Liu, Wei, Ma, Ting, Yalçin, Cansu, Hamadache, Rachika E., Salvi, Joaquim, Llado, Xavier, Estrada, Uma Maria Lal-Trehan, Abramova, Valeriia, Giancardo, Luca, Oliver, Arnau, Liu, Jialu, Huang, Haibin, Cui, Yue, Lin, Zehang, Liu, Yusheng, Zhu, Shunzhi, Patel, Tatsat R., Tutino, Vincent M., Orouskhani, Maysam, Wang, Huayu, Mossa-Basha, Mahmud, Zhu, Chengcheng, Rokuss, Maximilian R., Kirchhoff, Yannick, Disch, Nico, Holzschuh, Julius, Isensee, Fabian, Maier-Hein, Klaus, Sato, Yuki, Hirsch, Sven, Wegener, Susanne, Menze, Bjoern
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, character
Externí odkaz:
http://arxiv.org/abs/2312.17670
Autor:
Galati, Francesco, Falcetta, Daniele, Cortese, Rosa, Casolla, Barbara, Prados, Ferran, Burgos, Ninon, Zuluaga, Maria A.
We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality, despite the wide range of available cerebrovascular imaging techniqu
Externí odkaz:
http://arxiv.org/abs/2309.06075
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
Binary neural networks (BNNs) are an attractive solution for developing and deploying deep neural network (DNN)-based applications in resource constrained devices. Despite their success, BNNs still suffer from a fixed and limited compression factor t
Externí odkaz:
http://arxiv.org/abs/2306.13515
We propose the first joint-task learning framework for brain and vessel segmentation (JoB-VS) from Time-of-Flight Magnetic Resonance images. Unlike state-of-the-art vessel segmentation methods, our approach avoids the pre-processing step of implement
Externí odkaz:
http://arxiv.org/abs/2304.07744
Autor:
Dang, Vien Ngoc, Cascarano, Anna, Mulder, Rosa H., Cecil, Charlotte, Zuluaga, Maria A., Hernández-González, Jerónimo, Lekadir, Karim
A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models
Externí odkaz:
http://arxiv.org/abs/2211.05321
Autor:
Schiavone, Riccardo, Zuluaga, Maria A.
Binary neural networks (BNNs) have demonstrated their ability to solve complex tasks with comparable accuracy as full-precision deep neural networks (DNNs), while also reducing computational power and storage requirements and increasing the processin
Externí odkaz:
http://arxiv.org/abs/2207.04974
Publikováno v:
Pattern Recognition Volume 132, December 2022,108945
Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attention. This trend has led researchers to compare only learning-based meth
Externí odkaz:
http://arxiv.org/abs/2204.01637