Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Cem, M."'
Current methods for predicting osteoarthritis (OA) outcomes do not incorporate disease specific prior knowledge to improve the outcome prediction models. We developed a novel approach that effectively uses consecutive imaging studies to improve OA ou
Externí odkaz:
http://arxiv.org/abs/2406.10119
Autor:
Zhang, Chaojie, Chen, Shengjia, Cigdem, Ozkan, Rajamohan, Haresh Rengaraj, Cho, Kyunghyun, Kijowski, Richard, Deniz, Cem M.
A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI). The model incorporates the ImageNet pre-training and captures three-dimensional (3D) spatial co
Externí odkaz:
http://arxiv.org/abs/2405.02784
Autor:
Cigdem, Ozkan, Chen, Shengjia, Zhang, Chaojie, Cho, Kyunghyun, Kijowski, Richard, Deniz, Cem M.
A survival analysis model for predicting time-to-total knee replacement (TKR) was developed using features from medical images and clinical measurements. Supervised and self-supervised deep learning approaches were utilized to extract features from r
Externí odkaz:
http://arxiv.org/abs/2405.00069
Autor:
Unsal, Cem M, Topaloglu, Rasit Onur
Generalized Discrete Logarithm Problem (GDLP) is an extension of the Discrete Logarithm Problem where the goal is to find $x\in\mathbb{Z}_s$ such $g^x\mod s=y$ for a given $g,y\in\mathbb{Z}_s$. Generalized discrete logarithm is similar but instead of
Externí odkaz:
http://arxiv.org/abs/2212.12577
Autor:
Unsal, Cem M., Brady, Lucas T.
One of the leading candidates for near-term quantum advantage is the class of Variational Quantum Algorithms, but these algorithms suffer from classical difficulty in optimizing the variational parameters as the number of parameters increases. Theref
Externí odkaz:
http://arxiv.org/abs/2211.13767
Autor:
Unsal, Cem M., Oruc, A. Yavuz
We present quantum algorithms for routing concentration assignments on full capacity fat-and-slim concentrators, bounded fat-and-slim concentrators, and regular fat-and-slim concentrators. Classically, the concentration assignment takes $O(n)$ time o
Externí odkaz:
http://arxiv.org/abs/2103.09818
Publikováno v:
In Informatics in Medicine Unlocked 2024 45
Publikováno v:
Informatics in Medicine Unlocked, Vol 45, Iss , Pp 101444- (2024)
Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation, and plann
Externí odkaz:
https://doaj.org/article/0bcb7a6394f44bb49f96e0964ac6d914
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:
Haresh Rengaraj Rajamohan, Tianyu Wang, Kevin Leung, Gregory Chang, Kyunghyun Cho, Richard Kijowski, Cem M. Deniz
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA t
Externí odkaz:
https://doaj.org/article/2be54c251e244660a047431667f0df68