Zobrazeno 1 - 10
of 9 998
pro vyhledávání: '"Durak, 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:
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
Testing is an essential tool to assure software, especially so in safety-critical applications. To quantify how thoroughly a software item has been tested, a test coverage metric is required. Maybe the strictest such metric known in the safety critic
Externí odkaz:
http://arxiv.org/abs/2409.08708
Autor:
Jha, Debesh, Tomar, Nikhil Kumar, Sharma, Vanshali, Trinh, Quoc-Huy, Biswas, Koushik, Pan, Hongyi, Jha, Ritika K., Durak, Gorkem, Hann, Alexander, Varkey, Jonas, Dao, Hang Viet, Van Dao, Long, Nguyen, Binh Phuc, Pham, Khanh Cong, Tran, Quang Trung, Papachrysos, Nikolaos, Rieders, Brandon, Schmidt, Peter Thelin, Geissler, Enrik, Berzin, Tyler, Halvorsen, Pål, Riegler, Michael A., de Lange, Thomas, Bagci, Ulas
Colonoscopy is the primary method for examination, detection, and removal of polyps. Regular screening helps detect and prevent colorectal cancer at an early curable stage. However, challenges such as variation among the endoscopists' skills, bowel q
Externí odkaz:
http://arxiv.org/abs/2409.00045
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
Autor:
Gorade, Vandan, Susladkar, Onkar, Durak, Gorkem, Keles, Elif, Aktas, Ertugrul, Cebeci, Timurhan, Medetalibeyoglu, Alpay, Ladner, Daniela, Jha, Debesh, Bagci, Ulas
Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature interactions and generalize across
Externí odkaz:
http://arxiv.org/abs/2408.04491
Autor:
Peng, Linkai, Zhang, Zheyuan, Durak, Gorkem, Miller, Frank H., Medetalibeyoglu, Alpay, Wallace, Michael B., Bagci, Ulas
Pancreatic cancer remains one of the leading causes of cancer-related mortality worldwide. Precise segmentation of pancreatic tumors from medical images is a bottleneck for effective clinical decision-making. However, achieving a high accuracy is oft
Externí odkaz:
http://arxiv.org/abs/2407.19284
Autor:
Zhang, Zheyuan, Keles, Elif, Durak, Gorkem, Taktak, Yavuz, Susladkar, Onkar, Gorade, Vandan, Jha, Debesh, Ormeci, Asli C., Medetalibeyoglu, Alpay, Yao, Lanhong, Wang, Bin, Isler, Ilkin Sevgi, Peng, Linkai, Pan, Hongyi, Vendrami, Camila Lopes, Bourhani, Amir, Velichko, Yury, Gong, Boqing, Spampinato, Concetto, Pyrros, Ayis, Tiwari, Pallavi, Klatte, Derk C. F., Engels, Megan, Hoogenboom, Sanne, Bolan, Candice W., Agarunov, Emil, Harfouch, Nassier, Huang, Chenchan, Bruno, Marco J., Schoots, Ivo, Keswani, Rajesh N., Miller, Frank H., Gonda, Tamas, Yazici, Cemal, Tirkes, Temel, Turkbey, Baris, Wallace, Michael B., Bagci, Ulas
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, la
Externí odkaz:
http://arxiv.org/abs/2405.12367