Fetching the paper…
Reading the bibliography…
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions.
K. J. Cios and G. W. Moore, “Uniqueness of medical data mining,” Artificial Intelligence in Medicine , 2002
2002
Earlier work this paper cites.
A. Barbu, V. Athitsos, B. Georgescu, S. Boehm, P. Durlak, and D. Comaniciu, “Hierarchical learning of curves application to guidewire localization in fluoroscopy,” in CVPR , 2007
2007
Earlier work this paper cites.
A. Brost, A. Wimmer, R. Liao, J. Hornegger, and N. Strobel, “Catheter tracking: Filter-based vs. learning-based,” in DAGM , 2010
2010
Earlier work this paper cites.
Y. Ma, A. P. King, N. Gogin, C. A. Rinaldi, J. Gill, R. Razavi, and K. S. Rhode, “Real-time respiratory motion correction for cardiac electrophysiology procedures using image-based coronary sinus catheter tracking,” in MICCAI , 2010
2010
Earlier work this paper cites.
H. Lusic and M. W. Grinstaff, “X-ray-computed tomography contrast agents,” Chemical reviews , vol. 113, no. 3, pp. 1641–1666, 2013
2013
Earlier work this paper cites.
X. Wu, J. Housden, Y. Ma, B. Razavi, K. Rhode, and D. Rueckert, “Fast catheter segmentation from echocardiographic sequences based on segmentation from corresponding x-ray fluoroscopy for cardiac catheterization interventions,” IEEE Transactions on Medical Imaging , 2014
2014
Earlier work this paper cites.
H. Rafii-Tari, C. J. Payne, and G.-Z. Yang, “Current and emerging robot-assisted endovascular catheterization technologies: a review,” Annals of Biomedical Engineering , 2014
2014
Earlier work this paper cites.
T. Schlegl, J. Ofner, and G. Langs, “Unsupervised pre-training across image domains improves lung tissue classification,” in MICCAI Workshop , 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
C. Delmas, M.-O. Berger, E. Kerrien, C. Riddell, Y. Trousset, R. Anxionnat, and S. Bracard, “Three-dimensional curvilinear device reconstruction from two fluoroscopic views,” in SPIE , 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI , 2015
2015
Earlier work this paper cites.
P. Ambrosini, D. Ruijters, W. J. Niessen, A. Moelker, and T. van Walsum, “Fully automatic and real-time catheter segmentation in x-ray fluoroscopy,” in MICCAI , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Chen, R. Jafari, and N. Kehtarnavaz, “A survey of depth and inertial sensor fusion for human action recognition,” Multimedia Tools and Applications , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. A. Roth, D. Abate, K. H. Abate, S. M. Abay, C. Abbafati, N. Abbasi, H. Abbastabar, F. Abd-Allah, J. Abdela, A. Abdelalim, et al. , “Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the global burden of disease study 2017,” The Lancet , vol. 392, no. 10159, pp. 1736–1788, 2018
2018
Earlier work this paper cites.
N. Simaan, R. M. Yasin, and L. Wang, “Medical technologies and challenges of robot-assisted minimally invasive intervention and diagnostics,” Annual Review of Control, Robotics, and Autonomous Systems , 2018
2018
Earlier work this paper cites.
G. Dagnino, J. Liu, M. E. Abdelaziz, W. Chi, C. Riga, and G.-Z. Yang, “Haptic feedback and dynamic active constraints for robot-assisted endovascular catheterization,” in IROS , 2018
2018
Earlier work this paper cites.
K. Breininger, T. Würfl, T. Kurzendorfer, S. Albarqouni, M. Pfister, M. Kowarschik, N. Navab, and A. Maier, “Multiple device segmentation for fluoroscopic imaging using multi-task learning,” in MICCAI Workshop , 2018
2018
Earlier work this paper cites.
K. Breininger, S. Albarqouni, T. Kurzendorfer, M. Pfister, M. Kowarschik, and A. Maier, “Intraoperative stent segmentation in x-ray fluoroscopy for endovascular aortic repair,” International journal of computer assisted radiology and surgery , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, et al. , “Scaling egocentric vision: The epic-kitchens dataset,” in ECCV , 2018
2018
Earlier work this paper cites.
Y. Abu Farha, A. Richard, and J. Gall, “When will you do what?-anticipating temporal occurrences of activities,” in CVPR , 2018
2018
Earlier work this paper cites.
P. Schneider, Endovascular skills: guidewire and catheter skills for endovascular surgery . CRC press, 2019
2019
Earlier work this paper cites.
M. B. Molinero, G. Dagnino, J. Liu, W. Chi, M. E. Abdelaziz, T. M. Kwok, C. Riga, and G.-Z. Yang, “Haptic guidance for robot-assisted endovascular procedures: implementation and evaluation on surgical simulator,” in IROS , 2019
2019
Earlier work this paper cites.
M. Abdelaziz, D. Kundrat, M. Pupillo, G. Dagnino, T. Kwok, W. Chi, V. Groenhuis, F. Siepel, C. Riga, and S. Stramigioli, “Toward a versatile robotic platform for fluoroscopy and mri-guided endovascular interventions: A pre-clinical study,” in IROS , 2019
2019
Earlier work this paper cites.
I. Tobore, J. Li, L. Yuhang, Y. Al-Handarish, A. Kandwal, Z. Nie, L. Wang, et al. , “Deep learning intervention for health care challenges: some biomedical domain considerations,” JMIR mHealth and uHealth , 2019
2019
Earlier work this paper cites.
A. Furnari and G. M. Farinella, “What would you expect? anticipating egocentric actions with rolling-unrolling lstms and modality attention,” in ICCV , 2019
2019
Earlier work this paper cites.
N. Tufek, M. Yalcin, M. Altintas, F. Kalaoglu, Y. Li, and S. K. Bahadir, “Human action recognition using deep learning methods on limited sensory data,” IEEE Sensors Journal , 2019
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
X. Yang, X. Yang, M.-Y. Liu, F. Xiao, L. S. Davis, and J. Kautz, “Step: Spatio-temporal progressive learning for video action detection,” in CVPR , 2019, pp. 264–272
2019
Cited alongside, same era.
X. Yi, S. Adams, P. Babyn, and A. Elnajmi, “Automatic catheter and tube detection in pediatric x-ray images using a scale-recurrent network and synthetic data,” Journal of Digital Imaging , 2020
2020
Cited alongside, same era.
A. Nguyen, D. Kundrat, G. Dagnino, et al. , “End-to-end real-time catheter segmentation with optical flow-guided warping during endovascular intervention,” in ICRA , 2020
2020
Cited alongside, same era.
V. V. Danilov, D. Y. Kolpashchikov, O. M. Gerget, N. V. Laptev, A. Proutski, L. A. H. Gómez, F. Alvarez, and M. J. Ledesma-Carbayo, “Use of semi-synthetic data for catheter segmentation improvement,” Computerized Medical Imaging and Graphics , 2023
2023
Later among the works it cites.
J. Bos, D. Kundrat, and G. Dagnino, “Towards an action recognition framework for endovascular surgery,” in Annual International Conference of the IEEE Engineering in Medicine & Biology Society , 2023
2023
Later among the works it cites.
T. O. Akinyemi, O. M. Omisore, W. Du, W. Duan, X. Chen, G. Yi, and L. Wang, “Interventionalist hand motion recognition with convolutional neural network in robot-assisted coronary interventions,” IEEE Sensors Journal , 2023
2023
Later among the works it cites.
S. Wang, Z. Liu, W. Yang, Y. Cao, L. Zhao, and L. Xie, “Learning-based multimodal information fusion and behavior recognition of vascular interventionists’ operating skills,” IEEE Journal of Biomedical and Health Informatics , 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Chi, G. Dagnino, T. M. Kwok, A. Nguyen, D. Kundrat, M. E. Abdelaziz, C. Riga, C. Bicknell, and G.-Z. Yang, “Collaborative robot-assisted endovascular catheterization with generative adversarial imitation learning,” in ICRA , 2020
2020
Cited alongside, same era.
Y. Ma, S. Guo, C. Lyu, and Y. Wang, “Irregular motion recognition of guidewire in vascular interventional surgery,” in ICMA , 2020
2020
Cited alongside, same era.
R. Hisey, B. Chen, D. Camire, J. Erb, D. Howes, G. Fichtinger, and T. Ungi, “Recognizing workflow tasks in central venous catheterization using convolutional neural networks and reinforcement learning,” IJCARS , 2020
2020
Cited alongside, same era.
C. Lyu, S. Guo, Y. Ma, and Y. Wang, “A cnn-based method for guidewire tip collisions detection in vascular interventional surgery,” in ICMA , 2020
2020
Cited alongside, same era.
Y. Zhao, H. Xing, S. Guo, Y. Wang, J. Cui, Y. Ma, Y. Liu, X. Liu, J. Feng, and Y. Li, “A novel noncontact detection method of surgeon’s operation for a master-slave endovascular surgery robot,” Medical & Biological Engineering & Computing , 2020
2020
Cited alongside, same era.
H. Li, Y. Wang, R. Wan, S. Wang, T.-Q. Li, and A. Kot, “Domain generalization for medical imaging classification with linear-dependency regularization,” NeurIPS , 2020
2020
Cited alongside, same era.
F. Sener, D. Singhania, and A. Yao, “Temporal aggregate representations for long-range video understanding,” in ECCV , 2020
2020
Cited alongside, same era.
J. Guo, Z. Wang, and S. Guo, “A new collision detection algorithm for vascular interventional surgery simulation training system,” in ICMA , 2021
2021
Cited alongside, same era.
2023
Later among the works it cites.
N. Fischer, C. Marzi, K. Meisenbacher, A. Kisilenko, T. Davitashvili, M. Wagner, and F. Mathis-Ullrich, “A sensorized modular training platform to reduce vascular damage in endovascular surgery,” International Journal of Computer Assisted Radiology and Surgery , 2023
2023
Later among the works it cites.
Z. Zhong, D. Schneider, M. Voit, R. Stiefelhagen, and J. Beyerer, “Anticipative feature fusion transformer for multi-modal action anticipation,” in WACV , 2023
2023
Later among the works it cites.
S. Raab, I. Leibovitch, P. Li, K. Aberman, O. Sorkine-Hornung, and D. Cohen-Or, “Modi: Unconditional motion synthesis from diverse data,” in CVPR , 2023
2023
Later among the works it cites.
S. Atasever, N. Azginoglu, D. S. Terzi, and R. Terzi, “A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning,” Clinical Imaging , 2023
2023
Later among the works it cites.
Y. Luo, Z. Wang, Z. Chen, Z. Huang, and M. Baktashmotlagh, “Source-free progressive graph learning for open-set domain adaptation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
Y. Himeur, S. Al-Maadeed, H. Kheddar, N. Al-Maadeed, K. Abualsaud, A. Mohamed, and T. Khattab, “Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization,” Engineering Applications of Artificial Intelligence , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Moor, O. Banerjee, Z. S. H. Abad, H. M. Krumholz, J. Leskovec, E. J. Topol, and P. Rajpurkar, “Foundation models for generalist medical artificial intelligence,” Nature , 2023
2023
Later among the works it cites.
J. Qiu, L. Li, J. Sun, J. Peng, P. Shi, R. Zhang, Y. Dong, K. Lam, F. P.-W. Lo, B. Xiao, et al. , “Large ai models in health informatics: Applications, challenges, and the future,” IEEE Journal of Biomedical and Health Informatics , 2023
2023
Later among the works it cites.
J. Ma and B. Wang, “Segment anything in medical images,” arXiv preprint arXiv:2304.12306 , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
G. J. Faure, M.-H. Chen, and S.-H. Lai, “Holistic interaction transformer network for action detection,” in WACV , 2023
2023
Later among the works it cites.
Y. Shi, N. Wang, and X. Guo, “Yolov: making still image object detectors great at video object detection,” in AAAI , 2023
2023
Later among the works it cites.
Y. Fang, W. Wang, B. Xie, Q. Sun, L. Wu, X. Wang, T. Huang, X. Wang, and Y. Cao, “Eva: Exploring the limits of masked visual representation learning at scale,” in CVPR , 2023
2023
Later among the works it cites.
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in CVPR , 2023
2023
Later among the works it cites.
C. He, K. Li, Y. Zhang, L. Tang, Y. Zhang, Z. Guo, and X. Li, “Camouflaged object detection with feature decomposition and edge reconstruction,” in CVPR , 2023
2023
Later among the works it cites.
Y. Cao, J. Bin, J. Hamari, E. Blasch, and Z. Liu, “Multimodal object detection by channel switching and spatial attention,” in CVPR , 2023
2023
Later among the works it cites.
L. Li, J. Han, N. Zhang, N. Liu, S. Khan, H. Cholakkal, R. M. Anwer, and F. S. Khan, “Discriminative co-saliency and background mining transformer for co-salient object detection,” in CVPR , 2023
2023
Later among the works it cites.
H. Murtaza, M. Ahmed, N. F. Khan, G. Murtaza, S. Zafar, and A. Bano, “Synthetic data generation: State of the art in health care domain,” Computer Science Review , 2023
2023
Later among the works it cites.
Y. Xu, X. Zheng, Y. Li, X. Ye, H. Cheng, H. Wang, and J. Lyu, “Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review,” Journal of Evidence-Based Medicine , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Jianu, B. Huang, M. N. Vu, M. E. Abdelaziz, S. Fichera, C.-Y. Lee, P. Berthet-Rayne, F. R. y Baena, and A. Nguyen, “Cathsim: An open-source simulator for endovascular intervention,” IEEE Transactions on Medical Robotics and Bionics , 2024
2024
Closest in time.
2024
Closest in time.
N. L. Olsson, “Subtitle Edit,” Software, accessed: June 6th 2024. [Online]. Available: https://www.nikse.dk/subtitleedit
2024
Closest in time.
DarkLabel, “DarkLabel Annotation Tools,” Software, accessed: July 6th 2024. [Online]. Available: https://github.com/darkpgmr/DarkLabel
2024
Closest in time.