Zobrazeno 1 - 10
of 5 940
pro vyhledávání: '"VELICHKO, A. A."'
Autor:
Pan, Hongyi, Hong, Ziliang, Durak, Gorkem, Keles, Elif, Aktas, Halil Ertugrul, Taktak, Yavuz, Medetalibeyoglu, Alpay, Zhang, Zheyuan, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Miller, Frank, Keswani, Rajesh N., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification ut
Externí odkaz:
http://arxiv.org/abs/2411.05697
Autor:
Bengtsson, Max, Keles, Elif, Durak, Gorkem, Anwar, Syed, Velichko, Yuri S., Linguraru, Marius G., Waanders, Angela J., Bagci, Ulas
In this paper, we present a novel approach for segmenting pediatric brain tumors using a deep learning architecture, inspired by expert radiologists' segmentation strategies. Our model delineates four distinct tumor labels and is benchmarked on a hel
Externí odkaz:
http://arxiv.org/abs/2411.01390
Autor:
Pan, Hongyi, Durak, Gorkem, Zhang, Zheyuan, Taktak, Yavuz, Keles, Elif, Aktas, Halil Ertugrul, Medetalibeyoglu, Alpay, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Keswani, Rajesh N., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Goggins, Michael G., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face d
Externí odkaz:
http://arxiv.org/abs/2410.22530
Autor:
Konstantopoulou, Christina, De Cia, Annalisa, Krogager, Jens-Kristian, Ledoux, Cédric, Roman-Duval, Julia, Jenkins, Edward B., Ramburuth-Hurt, Tanita, Velichko, Anna
We present a novel method to characterize dust depletion, namely, the depletion of metals into dust grains. We used observed correlations among relative abundances combining a total of 17 metals in diverse galactic environments, including the Milky W
Externí odkaz:
http://arxiv.org/abs/2410.06155
Autor:
Jha, Debesh, Susladkar, Onkar Kishor, Gorade, Vandan, Keles, Elif, Antalek, Matthew, Seyithanoglu, Deniz, Cebeci, Timurhan, Aktas, Halil Ertugrul, Kartal, Gulbiz Dagoglu, Kaymakoglu, Sabahattin, Erturk, Sukru Mehmet, Velichko, Yuri, Ladner, Daniela, Borhani, Amir A., Medetalibeyoglu, Alpay, Durak, Gorkem, Bagci, Ulas
Liver cirrhosis, the end stage of chronic liver disease, is characterized by extensive bridging fibrosis and nodular regeneration, leading to an increased risk of liver failure, complications of portal hypertension, malignancy and death. Early diagno
Externí odkaz:
http://arxiv.org/abs/2410.16296
Autor:
Covelo-Paz, Alba, Giovinazzo, Emma, Oesch, Pascal A., Meyer, Romain A., Weibel, Andrea, Brammer, Gabriel, Fudamoto, Yoshinobu, Kerutt, Josephine, Lin, Jamie, Matharu, Jasleen, Naidu, Rohan P., Velichko, Anna, Bollo, Victoria, Bouwens, Rychard, Chisholm, John, Illingworth, Garth D., Kramarenko, Ivan, Magee, Daniel, Maseda, Michael, Matthee, Jorryt, Nelson, Erica, Reddy, Naveen, Schaerer, Daniel, Stefanon, Mauro, Xiao, Mengyuan
The H{\alpha} nebular emission line is an optimal tracer for recent star formation in galaxies. With the advent of JWST, this line has recently become observable at z>3 for the first time. We present a catalog of 1013 H{\alpha} emitters at 3.7
Externí odkaz:
http://arxiv.org/abs/2409.17241
Autor:
Borisenkov, Mikhail, Velichko, Andrei, Belyaev, Maksim, Korzun, Dmitry, Tserne, Tatyana, Bakutova, Larisa, Gubin, Denis
This study investigates machine learning algorithms to identify objective features for diagnosing food addiction (FA) and assessing confirmed symptoms (SC). Data were collected from 81 participants (mean age: 21.5 years, range: 18-61 years, women: 77
Externí odkaz:
http://arxiv.org/abs/2409.00310
Autor:
Biswas, Koushik, Pal, Ridal, Patel, Shaswat, Jha, Debesh, Karri, Meghana, Reza, Amit, Durak, Gorkem, Medetalibeyoglu, Alpay, Antalek, Matthew, Velichko, Yury, Ladner, Daniela, Borhani, Amir, Bagci, Ulas
Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans an
Externí odkaz:
http://arxiv.org/abs/2408.05692
In this study a new method for analyzing synchronization in oscillator systems is proposed using the example of modeling the dynamics of a circuit of two resistively coupled pulse oscillators. The dynamic characteristic of synchronization is fuzzy en
Externí odkaz:
http://arxiv.org/abs/2406.12906
Autor:
LaBella, Dominic, Schumacher, Katherine, Mix, Michael, Leu, Kevin, McBurney-Lin, Shan, Nedelec, Pierre, Villanueva-Meyer, Javier, Shapey, Jonathan, Vercauteren, Tom, Chia, Kazumi, Al-Salihi, Omar, Leu, Justin, Halasz, Lia, Velichko, Yury, Wang, Chunhao, Kirkpatrick, John, Floyd, Scott, Reitman, Zachary J., Mullikin, Trey, Bagci, Ulas, Sachdev, Sean, Hattangadi-Gluth, Jona A., Seibert, Tyler, Farid, Nikdokht, Puett, Connor, Pease, Matthew W., Shiue, Kevin, Anwar, Syed Muhammad, Faghani, Shahriar, Haider, Muhammad Ammar, Warman, Pranav, Albrecht, Jake, Jakab, András, Moassefi, Mana, Chung, Verena, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Huang, Christina, Coley, Aaron, Ghanta, Siddharth, Schneider, Alex, Sharp, Conrad, Saluja, Rachit, Kofler, Florian, Lohmann, Philipp, Vollmuth, Phillipp, Gagnon, Louis, Adewole, Maruf, Li, Hongwei Bran, Kazerooni, Anahita Fathi, Tahon, Nourel Hoda, Anazodo, Udunna, Moawad, Ahmed W., Menze, Bjoern, Linguraru, Marius George, Aboian, Mariam, Wiestler, Benedikt, Baid, Ujjwal, Conte, Gian-Marco, Rauschecker, Andreas M., Nada, Ayman, Abayazeed, Aly H., Huang, Raymond, de Verdier, Maria Correia, Rudie, Jeffrey D., Bakas, Spyridon, Calabrese, Evan
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target
Externí odkaz:
http://arxiv.org/abs/2405.18383