Zobrazeno 1 - 10
of 20 298
pro vyhledávání: '"A, Keswani"'
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:
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:
Tomar, Nikhil Kumar, Jha, Debesh, Biswas, Koushik, Berzin, Tyler M., Keswani, Rajesh, Wallace, Michael, Bagci, Ulas
Colorectal cancer (CRC) is the third most common cause of cancer diagnosed in the United States and the second leading cause of cancer-related death among both genders. Notably, CRC is the leading cause of cancer in younger men less than 50 years old
Externí odkaz:
http://arxiv.org/abs/2409.05875
Autor:
Boerstler, Kyle, Keswani, Vijay, Chan, Lok, Borg, Jana Schaich, Conitzer, Vincent, Heidari, Hoda, Sinnott-Armstrong, Walter
Preference elicitation frameworks feature heavily in the research on participatory ethical AI tools and provide a viable mechanism to enquire and incorporate the moral values of various stakeholders. As part of the elicitation process, surveys about
Externí odkaz:
http://arxiv.org/abs/2408.02862
Autor:
Keswani, Vijay, Conitzer, Vincent, Heidari, Hoda, Borg, Jana Schaich, Sinnott-Armstrong, Walter
Computational preference elicitation methods are tools used to learn people's preferences quantitatively in a given context. Recent works on preference elicitation advocate for active learning as an efficient method to iteratively construct queries (
Externí odkaz:
http://arxiv.org/abs/2407.18889
Autor:
Chakali Bramhayya
Publikováno v:
Governance and Politics. 2:84-89
Book Review: Locating BRICS in the Global Order: Perspectives from the Global South. (2023). Ed. by Rajan Kumar, Meeta Keswani Mehra, G. Venkat Raman, Meenakshi Sundriyal. Routledge India. 258 p.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::90adb8a1d43d7507fabbe6267b24c022
https://doi.org/10.24833/2782-7062-2023-2-1-84-89
https://doi.org/10.24833/2782-7062-2023-2-1-84-89
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
Akademický článek
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In many predictive contexts (e.g., credit lending), true outcomes are only observed for samples that were positively classified in the past. These past observations, in turn, form training datasets for classifiers that make future predictions. Howeve
Externí odkaz:
http://arxiv.org/abs/2402.11338