Zobrazeno 1 - 10
of 430
pro vyhledávání: '"Yuille, A. L."'
Autor:
Li, Jianning, Zhou, Zongwei, Yang, Jiancheng, Pepe, Antonio, Gsaxner, Christina, Luijten, Gijs, Qu, Chongyu, Zhang, Tiezheng, Chen, Xiaoxi, Li, Wenxuan, Wodzinski, Marek, Friedrich, Paul, Xie, Kangxian, Jin, Yuan, Ambigapathy, Narmada, Nasca, Enrico, Solak, Naida, Melito, Gian Marco, Vu, Viet Duc, Memon, Afaque R., Schlachta, Christopher, De Ribaupierre, Sandrine, Patel, Rajnikant, Eagleson, Roy, Chen, Xiaojun, Mächler, Heinrich, Kirschke, Jan Stefan, de la Rosa, Ezequiel, Christ, Patrick Ferdinand, Li, Hongwei Bran, Ellis, David G., Aizenberg, Michele R., Gatidis, Sergios, Küstner, Thomas, Shusharina, Nadya, Heller, Nicholas, Andrearczyk, Vincent, Depeursinge, Adrien, Hatt, Mathieu, Sekuboyina, Anjany, Löffler, Maximilian, Liebl, Hans, Dorent, Reuben, Vercauteren, Tom, Shapey, Jonathan, Kujawa, Aaron, Cornelissen, Stefan, Langenhuizen, Patrick, Ben-Hamadou, Achraf, Rekik, Ahmed, Pujades, Sergi, Boyer, Edmond, Bolelli, Federico, Grana, Costantino, Lumetti, Luca, Salehi, Hamidreza, Ma, Jun, Zhang, Yao, Gharleghi, Ramtin, Beier, Susann, Sowmya, Arcot, Garza-Villarreal, Eduardo A., Balducci, Thania, Angeles-Valdez, Diego, Souza, Roberto, Rittner, Leticia, Frayne, Richard, Ji, Yuanfeng, Ferrari, Vincenzo, Chatterjee, Soumick, Dubost, Florian, Schreiber, Stefanie, Mattern, Hendrik, Speck, Oliver, Haehn, Daniel, John, Christoph, Nürnberger, Andreas, Pedrosa, João, Ferreira, Carlos, Aresta, Guilherme, Cunha, António, Campilho, Aurélio, Suter, Yannick, Garcia, Jose, Lalande, Alain, Vandenbossche, Vicky, Van Oevelen, Aline, Duquesne, Kate, Mekhzoum, Hamza, Vandemeulebroucke, Jef, Audenaert, Emmanuel, Krebs, Claudia, van Leeuwen, Timo, Vereecke, Evie, Heidemeyer, Hauke, Röhrig, Rainer, Hölzle, Frank, Badeli, Vahid, Krieger, Kathrin, Gunzer, Matthias, Chen, Jianxu, van Meegdenburg, Timo, Dada, Amin, Balzer, Miriam, Fragemann, Jana, Jonske, Frederic, Rempe, Moritz, Malorodov, Stanislav, Bahnsen, Fin H., Seibold, Constantin, Jaus, Alexander, Marinov, Zdravko, Jaeger, Paul F., Stiefelhagen, Rainer, Santos, Ana Sofia, Lindo, Mariana, Ferreira, André, Alves, Victor, Kamp, Michael, Abourayya, Amr, Nensa, Felix, Hörst, Fabian, Brehmer, Alexander, Heine, Lukas, Hanusrichter, Yannik, Weßling, Martin, Dudda, Marcel, Podleska, Lars E., Fink, Matthias A., Keyl, Julius, Tserpes, Konstantinos, Kim, Moon-Sung, Elhabian, Shireen, Lamecker, Hans, Zukić, Dženan, Paniagua, Beatriz, Wachinger, Christian, Urschler, Martin, Duong, Luc, Wasserthal, Jakob, Hoyer, Peter F., Basu, Oliver, Maal, Thomas, Witjes, Max J. H., Schiele, Gregor, Chang, Ti-chiun, Ahmadi, Seyed-Ahmad, Luo, Ping, Menze, Bjoern, Reyes, Mauricio, Deserno, Thomas M., Davatzikos, Christos, Puladi, Behrus, Fua, Pascal, Yuille, Alan L., Kleesiek, Jens, Egger, Jan
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit s
Externí odkaz:
http://arxiv.org/abs/2308.16139
Autor:
Chen, Liangyu, Bai, Yutong, Huang, Siyu, Lu, Yongyi, Wen, Bihan, Yuille, Alan L., Zhou, Zongwei
Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first. However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as ran
Externí odkaz:
http://arxiv.org/abs/2210.02442
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. The development of automatic segmentation systems from multi-phase CT for splenic vascular injury can augment severity grading for improving clinical decision supp
Externí odkaz:
http://arxiv.org/abs/2201.00942
Autor:
Xiang, Tiange, Zhang, Yixiao, Lu, Yongyi, Yuille, Alan L., Zhang, Chaoyi, Cai, Weidong, Zhou, Zongwei
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, we propose the use of Space-aware Mem
Externí odkaz:
http://arxiv.org/abs/2111.13495
Autor:
Kang, Mintong, Li, Bowen, Zhu, Zengle, Lu, Yongyi, Fishman, Elliot K., Yuille, Alan L., Zhou, Zongwei
The success of deep learning relies heavily on large labeled datasets, but we often only have access to several small datasets associated with partial labels. To address this problem, we propose a new initiative, "Label-Assemble", that aims to unleas
Externí odkaz:
http://arxiv.org/abs/2109.12265
Autor:
Weber, Mark, Wang, Huiyu, Qiao, Siyuan, Xie, Jun, Collins, Maxwell D., Zhu, Yukun, Yuan, Liangzhe, Kim, Dahun, Yu, Qihang, Cremers, Daniel, Leal-Taixe, Laura, Yuille, Alan L., Schroff, Florian, Adam, Hartwig, Chen, Liang-Chieh
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision. DeepLab2 includes all our recently developed DeepLab model va
Externí odkaz:
http://arxiv.org/abs/2106.09748
Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing tren
Externí odkaz:
http://arxiv.org/abs/2105.14773
Human body is a complex dynamic system composed of various sub-dynamic parts. Especially, thoracic and abdominal organs have complex internal shape variations with different frequencies by various reasons such as respiration with fast motion and peri
Externí odkaz:
http://arxiv.org/abs/2103.05525
Autor:
Chen, Jieneng, Yan, Ke, Zhang, Yu-Dong, Tang, Youbao, Xu, Xun, Sun, Shuwen, Liu, Qiuping, Huang, Lingyun, Xiao, Jing, Yuille, Alan L., Zhang, Ya, Lu, Le
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich semantics: capsular invasion, visibility, smoothness, folding and protuberance, etc. Capsular invasion on tumor boundary has proven to be clinically correlated with the prognosti
Externí odkaz:
http://arxiv.org/abs/2103.05170
Autor:
Chen, Jieneng, Lu, Yongyi, Yu, Qihang, Luo, Xiangde, Adeli, Ehsan, Wang, Yan, Lu, Le, Yuille, Alan L., Zhou, Yuyin
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has becom
Externí odkaz:
http://arxiv.org/abs/2102.04306