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pro vyhledávání: '"Ribalta, Pablo"'
We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge. To find the optimal model architecture and the learning schedule, we have run an extensive ablation study to test: deep supervision loss, Focal l
Externí odkaz:
http://arxiv.org/abs/2110.03352
Autor:
Karargyris, Alexandros, Umeton, Renato, Sheller, Micah J., Aristizabal, Alejandro, George, Johnu, Bala, Srini, Beutel, Daniel J., Bittorf, Victor, Chaudhari, Akshay, Chowdhury, Alexander, Coleman, Cody, Desinghu, Bala, Diamos, Gregory, Dutta, Debo, Feddema, Diane, Fursin, Grigori, Guo, Junyi, Huang, Xinyuan, Kanter, David, Kashyap, Satyananda, Lane, Nicholas, Mallick, Indranil, Mascagni, Pietro, Mehta, Virendra, Natarajan, Vivek, Nikolov, Nikola, Padoy, Nicolas, Pekhimenko, Gennady, Reddi, Vijay Janapa, Reina, G Anthony, Ribalta, Pablo, Rosenthal, Jacob, Singh, Abhishek, Thiagarajan, Jayaraman J., Wuest, Anna, Xenochristou, Maria, Xu, Daguang, Yadav, Poonam, Rosenthal, Michael, Loda, Massimo, Johnson, Jason M., Mattson, Peter
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential re
Externí odkaz:
http://arxiv.org/abs/2110.01406
Akademický článek
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