Search Results - "Akünal, Ünal"

Real-world federated learning in radiology: hurdles to overcome and benefits to gain by Bujotzek, Markus Ralf, Akünal, Ünal, Denner, Stefan, Neher, Peter, Zenk, Maximilian, Frodl, Eric, Jaiswal, Astha, Kim, Moon, Krekiehn, Nicolai R, Nickel, Manuel, Ruppel, Richard, Both, Marcus, Döllinger, Felix, Opitz, Marcel, Persigehl, Thorsten, Kleesiek, Jens, Penzkofer, Tobias, Maier-Hein, Klaus, Bucher, Andreas, Braren, Rickmer

“…Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated…”
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Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge by Schmidt, Kendall, Bearce, Benjamin, Chang, Ken, Coombs, Laura, Farahani, Keyvan, Elbatel, Marawan, Mouheb, Kaouther, Marti, Robert, Zhang, Ruipeng, Zhang, Yao, Wang, Yanfeng, Hu, Yaojun, Ying, Haochao, Xu, Yuyang, Testagrose, Conrad, Demirer, Mutlu, Gupta, Vikash, Akünal, Ünal, Bujotzek, Markus, Maier-Hein, Klaus H., Qin, Yi, Li, Xiaomeng, Kalpathy-Cramer, Jayashree, Roth, Holger R.

“…The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast…”
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Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge by Schmidt, Kendall, Bearce, Benjamin, Chang, Ken, Coombs, Laura, Farahani, Keyvan, Elbatele, Marawan, Mouhebe, Kaouther, Marti, Robert, Zhang, Ruipeng, Zhang, Yao, Wang, Yanfeng, Hu, Yaojun, Ying, Haochao, Xu, Yuyang, Testagrose, Conrad, Demirer, Mutlu, Gupta, Vikash, Akünal, Ünal, Bujotzek, Markus, Maier-Hein, Klaus H, Qin, Yi, Li, Xiaomeng, Kalpathy-Cramer, Jayashree, Roth, Holger R

“…Medical Image Analysis Volume 95, July 2024, 103206 The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has…”
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Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain by Bujotzek, Markus R, Akünal, Ünal, Denner, Stefan, Neher, Peter, Zenk, Maximilian, Frodl, Eric, Jaiswal, Astha, Kim, Moon, Krekiehn, Nicolai R, Nickel, Manuel, Ruppel, Richard, Both, Marcus, Döllinger, Felix, Opitz, Marcel, Persigehl, Thorsten, Kleesiek, Jens, Penzkofer, Tobias, Maier-Hein, Klaus, Braren, Rickmer, Bucher, Andreas

“…Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in…”
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