Zobrazeno 1 - 10
of 8 702
pro vyhledávání: '"Klaus, H."'
Autor:
Lasheras-Hernandez, Blanca, Strobl, Klaus H., Izquierdo, Sergio, Bodenmüller, Tim, Triebel, Rudolph, Civera, Javier
Metric depth estimation from visual sensors is crucial for robots to perceive, navigate, and interact with their environment. Traditional range imaging setups, such as stereo or structured light cameras, face hassles including calibration, occlusions
Externí odkaz:
http://arxiv.org/abs/2412.02386
Autor:
Isensee, Fabian, Kirchhoff, Yannick, Kraemer, Lars, Rokuss, Maximilian, Ulrich, Constantin, Maier-Hein, Klaus H.
This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge. We leveraged the nnU-Net ResEnc L model, introd
Externí odkaz:
http://arxiv.org/abs/2411.17213
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:
Wald, Tassilo, Ulrich, Constantin, Köhler, Gregor, Zimmerer, David, Denner, Stefan, Baumgartner, Michael, Isensee, Fabian, Jaini, Priyank, Maier-Hein, Klaus H.
What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to
Externí odkaz:
http://arxiv.org/abs/2410.23107
Autor:
Kovacs, Balint, Xiao, Shuhan, Rokuss, Maximilian, Ulrich, Constantin, Isensee, Fabian, Maier-Hein, Klaus H.
The third autoPET challenge introduced a new data-centric task this year, shifting the focus from model development to improving metastatic lesion segmentation on PET/CT images through data quality and handling strategies. In response, we developed t
Externí odkaz:
http://arxiv.org/abs/2409.10120
Autor:
Rokuss, Maximilian, Kovacs, Balint, Kirchhoff, Yannick, Xiao, Shuhan, Ulrich, Constantin, Maier-Hein, Klaus H., Isensee, Fabian
Automated lesion segmentation in PET/CT scans is crucial for improving clinical workflows and advancing cancer diagnostics. However, the task is challenging due to physiological variability, different tracers used in PET imaging, and diverse imaging
Externí odkaz:
http://arxiv.org/abs/2409.09478
Autor:
Traub, Jeremias, Bungert, Till J., Lüth, Carsten T., Baumgartner, Michael, Maier-Hein, Klaus H., Maier-Hein, Lena, Jaeger, Paul F
Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these sys
Externí odkaz:
http://arxiv.org/abs/2407.01032
Autor:
Xiao, Shuhan, Klein, Lukas, Petersen, Jens, Vollmuth, Philipp, Jaeger, Paul F., Maier-Hein, Klaus H.
Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. These covariates, referred to as biomarkers, are extracted from pre-treat
Externí odkaz:
http://arxiv.org/abs/2406.02534
Autor:
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.
Publikováno v:
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 been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography system
Externí odkaz:
http://arxiv.org/abs/2405.14900
Autor:
Ulrich, Constantin, Knobloch, Catherine, Holzschuh, Julius C., Wald, Tassilo, Rokuss, Maximilian R., Zenk, Maximilian, Fischer, Maximilian, Baumgartner, Michael, Isensee, Fabian, Maier-Hein, Klaus H.
Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions persists. While domain generalization is acknowledged as vital for robust
Externí odkaz:
http://arxiv.org/abs/2404.15718