Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Calivà, Francesco"'
Self-supervised speech representation learning (S3RL) is revolutionizing the way we leverage the ever-growing availability of data. While S3RL related studies typically use large models, we employ light-weight networks to comply with tight memory of
Externí odkaz:
http://arxiv.org/abs/2303.04255
Autor:
Macha, Sashank, Oza, Om, Escott, Alex, Caliva, Francesco, Armitano, Robbie, Cheekatmalla, Santosh Kumar, Parthasarathi, Sree Hari Krishnan, Liu, Yuzong
Publikováno v:
ICASSP 2023
Fixed-point (FXP) inference has proven suitable for embedded devices with limited computational resources, and yet model training is continually performed in floating-point (FLP). FXP training has not been fully explored and the non-trivial conversio
Externí odkaz:
http://arxiv.org/abs/2303.02284
Deep Learning (DL) has shown potential in accelerating Magnetic Resonance Image acquisition and reconstruction. Nevertheless, there is a dearth of tailored methods to guarantee that the reconstruction of small features is achieved with high fidelity.
Externí odkaz:
http://arxiv.org/abs/2011.00070
Autor:
Desai, Arjun D., Caliva, Francesco, Iriondo, Claudia, Khosravan, Naji, Mortazi, Aliasghar, Jambawalikar, Sachin, Torigian, Drew, Ellermann, Jutta, Akcakaya, Mehmet, Bagci, Ulas, Tibrewala, Radhika, Flament, Io, O`Brien, Matthew, Majumdar, Sharmila, Perslev, Mathias, Pai, Akshay, Igel, Christian, Dam, Erik B., Gaj, Sibaji, Yang, Mingrui, Nakamura, Kunio, Li, Xiaojuan, Deniz, Cem M., Juras, Vladimir, Regatte, Ravinder, Gold, Garry E., Hargreaves, Brian A., Pedoia, Valentina, Chaudhari, Akshay S.
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI
Externí odkaz:
http://arxiv.org/abs/2004.14003
Autor:
Namiri, Nikan K., Flament, Io, Astuto, Bruno, Shah, Rutwik, Tibrewala, Radhika, Caliva, Francesco, Link, Thomas M., Pedoia, Valentina, Majumdar, Sharmila
Purpose: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. Materials and Methods: This retrospective analysis was conducted on 1243 knee MR images (1008 i
Externí odkaz:
http://arxiv.org/abs/2003.09089
Publikováno v:
In JSES International September 2023 7(5):861-867
Autor:
Cheng, Kaiyang, Iriondo, Claudia, Calivá, Francesco, Krogue, Justin, Majumdar, Sharmila, Pedoia, Valentina
The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and variance of the training dataset. However, implementing this approach with c
Externí odkaz:
http://arxiv.org/abs/1909.04108
Autor:
Caliva, Francesco, Iriondo, Claudia, Martinez, Alejandro Morales, Majumdar, Sharmila, Pedoia, Valentina
Convolutional neural networks for semantic segmentation suffer from low performance at object boundaries. In medical imaging, accurate representation of tissue surfaces and volumes is important for tracking of disease biomarkers such as tissue morpho
Externí odkaz:
http://arxiv.org/abs/1908.03679
Autor:
Namiri, Nikan K., Càliva, Francesco, Martinez, Alejandro Morales, Pedoia, Valentina, Lansdown, Drew A.
Publikováno v:
In Arthroscopy: The Journal of Arthroscopic and Related Surgery June 2023 39(6):1493-1501
Autor:
Ribeiro, Fabio De Sousa, Caliva, Francesco, Swainson, Mark, Gudmundsson, Kjartan, Leontidis, Georgios, Kollias, Stefanos
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industrial applications, the requirement for large amounts of annotated data is
Externí odkaz:
http://arxiv.org/abs/1812.01681