Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Abel Diaz"'
Publikováno v:
IEEE Access, Vol 12, Pp 10120-10134 (2024)
Large language models provide high-accuracy solutions in many natural language processing tasks. In particular, they are used as word embeddings in sentiment analysis models. However, these models pick up on and amplify biases and social stereotypes
Externí odkaz:
https://doaj.org/article/0bf6b4166d434aabb73d28394f148412
Autor:
Abel Diaz Berenguer, Meshia Cedric Oveneke, Habib-Ur-Rehman Khalid, Mitchel Alioscha-Perez, Andre Bourdoux, Hichem Sahli
Publikováno v:
IEEE Access, Vol 7, Pp 137122-137135 (2019)
In this paper we propose a novel framework to process Doppler-radar signals for hand gesture recognition. Doppler-radar sensors provide many advantages over other emerging sensing modalities, including low development costs and high sensitivity to ca
Externí odkaz:
https://doaj.org/article/d65d5ed6d1e74c108b771f88c946393a
Autor:
Boulogne, Luuk H., Lorenz, Julian, Kienzle, Daniel, Schon, Robin, Ludwig, Katja, Lienhart, Rainer, Jegou, Simon, Li, Guang, Chen, Cong, Wang, Qi, Shi, Derik, Maniparambil, Mayug, Muller, Dominik, Mertes, Silvan, Schroter, Niklas, Hellmann, Fabio, Elia, Miriam, Dirks, Ine, Bossa, Matias Nicolas, Berenguer, Abel Diaz, Mukherjee, Tanmoy, Vandemeulebroucke, Jef, Sahli, Hichem, Deligiannis, Nikos, Gonidakis, Panagiotis, Huynh, Ngoc Dung, Razzak, Imran, Bouadjenek, Reda, Verdicchio, Mario, Borrelli, Pasquale, Aiello, Marco, Meakin, James A., Lemm, Alexander, Russ, Christoph, Ionasec, Razvan, Paragios, Nikos, van Ginneken, Bram, Dubois, Marie-Pierre Revel
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains abse
Externí odkaz:
http://arxiv.org/abs/2306.10484
Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation learning. However, the inability of deep models to generalize to unseen
Externí odkaz:
http://arxiv.org/abs/2207.01437
Autor:
Boulogne, Luuk H., Lorenz, Julian, Kienzle, Daniel, Schön, Robin, Ludwig, Katja, Lienhart, Rainer, Jégou, Simon, Li, Guang, Chen, Cong, Wang, Qi, Shi, Derik, Maniparambil, Mayug, Müller, Dominik, Mertes, Silvan, Schröter, Niklas, Hellmann, Fabio, Elia, Miriam, Dirks, Ine, Bossa, Matías Nicolás, Berenguer, Abel Díaz, Mukherjee, Tanmoy, Vandemeulebroucke, Jef, Sahli, Hichem, Deligiannis, Nikos, Gonidakis, Panagiotis, Huynh, Ngoc Dung, Razzak, Imran, Bouadjenek, Reda, Verdicchio, Mario, Borrelli, Pasquale, Aiello, Marco, Meakin, James A., Lemm, Alexander, Russ, Christoph, Ionasec, Razvan, Paragios, Nikos, van Ginneken, Bram, Revel, Marie-Pierre
Publikováno v:
In Medical Image Analysis October 2024 97
Autor:
Berenguer, Abel Díaz, Kvasnytsia, Maryna, Bossa, Matías Nicolás, Mukherjee, Tanmoy, Deligiannis, Nikos, Sahli, Hichem
Publikováno v:
In Medical Image Analysis May 2024 94
Publikováno v:
In Expert Systems With Applications February 2024 236
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Autor:
Berenguer, Abel Díaz, Sahli, Hichem, Joukovsky, Boris, Kvasnytsia, Maryna, Dirks, Ine, Alioscha-Perez, Mitchel, Deligiannis, Nikos, Gonidakis, Panagiotis, Sánchez, Sebastián Amador, Brahimetaj, Redona, Papavasileiou, Evgenia, Chana, Jonathan Cheung-Wai, Li, Fei, Song, Shangzhen, Yang, Yixin, Tilborghs, Sofie, Willems, Siri, Eelbode, Tom, Bertels, Jeroen, Vandermeulen, Dirk, Maes, Frederik, Suetens, Paul, Fidon, Lucas, Vercauteren, Tom, Robben, David, Brys, Arne, Smeets, Dirk, Ilsen, Bart, Buls, Nico, Watté, Nina, de Mey, Johan, Snoeckx, Annemiek, Parizel, Paul M., Guiot, Julien, Deprez, Louis, Meunier, Paul, Gryspeerdt, Stefaan, De Smet, Kristof, Jansen, Bart, Vandemeulebroucke, Jef
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extrac
Externí odkaz:
http://arxiv.org/abs/2011.11719
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