Zobrazeno 1 - 10
of 860
pro vyhledávání: '"Li, Wenxuan"'
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Autor:
Bassi, Pedro R. A. S., Li, Wenxuan, Tang, Yucheng, Isensee, Fabian, Wang, Zifu, Chen, Jieneng, Chou, Yu-Cheng, Kirchhoff, Yannick, Rokuss, Maximilian, Huang, Ziyan, Ye, Jin, He, Junjun, Wald, Tassilo, Ulrich, Constantin, Baumgartner, Michael, Roy, Saikat, Maier-Hein, Klaus H., Jaeger, Paul, Ye, Yiwen, Xie, Yutong, Zhang, Jianpeng, Chen, Ziyang, Xia, Yong, Xing, Zhaohu, Zhu, Lei, Sadegheih, Yousef, Bozorgpour, Afshin, Kumari, Pratibha, Azad, Reza, Merhof, Dorit, Shi, Pengcheng, Ma, Ting, Du, Yuxin, Bai, Fan, Huang, Tiejun, Zhao, Bo, Wang, Haonan, Li, Xiaomeng, Gu, Hanxue, Dong, Haoyu, Yang, Jichen, Mazurowski, Maciej A., Gupta, Saumya, Wu, Linshan, Zhuang, Jiaxin, Chen, Hao, Roth, Holger, Xu, Daguang, Blaschko, Matthew B., Decherchi, Sergio, Cavalli, Andrea, Yuille, Alan L., Zhou, Zongwei
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a con
Externí odkaz:
http://arxiv.org/abs/2411.03670
Autor:
Bassi, Pedro R. A. S., Wu, Qilong, Li, Wenxuan, Decherchi, Sergio, Cavalli, Andrea, Yuille, Alan, Zhou, Zongwei
As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and
Externí odkaz:
http://arxiv.org/abs/2411.02753
Optimizing black-box functions in high-dimensional search spaces has been known to be challenging for traditional Bayesian Optimization (BO). In this paper, we introduce HiBO, a novel hierarchical algorithm integrating global-level search space parti
Externí odkaz:
http://arxiv.org/abs/2410.23148
3D object detection in driving scenarios faces the challenge of complex road environments, which can lead to the loss or incompleteness of key features, thereby affecting perception performance. To address this issue, we propose an advanced detection
Externí odkaz:
http://arxiv.org/abs/2408.07999
Autor:
Li, Wenxuan, Qu, Chongyu, Chen, Xiaoxi, Bassi, Pedro R. A. S., Shi, Yijia, Lai, Yuxiang, Yu, Qian, Xue, Huimin, Chen, Yixiong, Lin, Xiaorui, Tang, Yutong, Cao, Yining, Han, Haoqi, Zhang, Zheyuan, Liu, Jiawei, Zhang, Tiezheng, Ma, Yujiu, Wang, Jincheng, Zhang, Guang, Yuille, Alan, Zhou, Zongwei
We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673K high-quality masks of anatomical
Externí odkaz:
http://arxiv.org/abs/2407.16697
Autor:
Xiao, Junfei, Zhou, Ziqi, Li, Wenxuan, Lan, Shiyi, Mei, Jieru, Yu, Zhiding, Yuille, Alan, Zhou, Yuyin, Xie, Cihang
This paper introduces ProLab, a novel approach using property-level label space for creating strong interpretable segmentation models. Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common s
Externí odkaz:
http://arxiv.org/abs/2312.13764
To protect the intellectual property of well-trained deep neural networks (DNNs), black-box watermarks, which are embedded into the prediction behavior of DNN models on a set of specially-crafted samples and extracted from suspect models using only A
Externí odkaz:
http://arxiv.org/abs/2309.03466
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
Publikováno v:
Int J Comput Vis (2024) 1-21
We have recently seen tremendous progress in diffusion advances for generating realistic human motions. Yet, they largely disregard the multi-human interactions. In this paper, we present InterGen, an effective diffusion-based approach that incorpora
Externí odkaz:
http://arxiv.org/abs/2304.05684
Autor:
Zhou, Zhijia1,2 (AUTHOR), Li, Wenxuan1,2 (AUTHOR), Wu, Yuelan1,2 (AUTHOR), Wang, Tao1,2 (AUTHOR), Zhang, Jinghao1 (AUTHOR), You, Liping1 (AUTHOR), Li, Haoran3 (AUTHOR), Zheng, Chao1 (AUTHOR) jimidarklove@126.com, Gao, Yueqiu1 (AUTHOR) gaoyueqiu@hotmail.com, Sun, Xuehua1 (AUTHOR) susan_sxh@shutcm.edu.cn
Publikováno v:
Scientific Reports. 11/16/2024, Vol. 14 Issue 1, p1-10. 10p.
