Zobrazeno 1 - 10
of 12 116
pro vyhledávání: '"KELEŞ ON"'
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
The Bj{\o}ntegaard Delta (BD) measure is widely employed to evaluate and quantify the variations in the rate-distortion(RD) performance across different codecs. Many researchers report the average BD value over multiple videos within a dataset for di
Externí odkaz:
http://arxiv.org/abs/2409.08772
Context: Test engineers are looking at more ways to test systems more effectively and efficiently. With recent advances in the field of AI (Artificial Intelligence), a large number of AI-powered test automation tools have emerged, which can help make
Externí odkaz:
http://arxiv.org/abs/2409.00411
There are many widely used tools for measuring test-coverage and code-coverage. Test coverage is the ratio of requirements or other non-code artifacts covered by a test suite, while code-coverage is the ratio of source code covered by tests. Almost a
Externí odkaz:
http://arxiv.org/abs/2408.06148
Autor:
Garousi, Vahid, Keleş, Alper Buğra
Alongside software testing education in universities, a great extent of effort and resources are spent on software-testing training activities in industry. For example, there are several international certification schemes in testing, such as those p
Externí odkaz:
http://arxiv.org/abs/2408.06144
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
Machine translation is indispensable in healthcare for enabling the global dissemination of medical knowledge across languages. However, complex medical terminology poses unique challenges to achieving adequate translation quality and accuracy. This
Externí odkaz:
http://arxiv.org/abs/2407.12126