|
[1]Orson W. Moe, “ Kidney stones: pathophysiology and medical management,” The lancet 367.9507 (2006): 333-344.
[2]Tilahun Alelign and Beyene Petros. “Kidney stone disease: an update on current concepts,” Advances in urology 2018 (2018).
[3]Kevin R. Anderson, et al., “Optimal therapy for the distal ureteral stone: extracorporeal shock wave lithotripsy versus ureteroscopy,” The Journal of urology 152.1 (1994): 62-65.
[4]Yahong Xu, et al., “A meta-analysis of the efficacy of ureteroscopic lithotripsy and extracorporeal shock wave lithotripsy on ureteral calculi,” Acta cirurgica brasileira 29 (2014): 346-352.
[5]Omar M. Aboumarzouk, et al., “Extracorporeal shock wave lithotripsy (ESWL) versus ureteroscopic management for ureteric calculi,” Cochrane Database of Systematic Reviews 12 (2011).
[6]Markus J.,Baderet al., “Contemporary management of ureteral stones,” European Urology 61.4 (2012): 764-772.
[7]Klas,Lindqvist et al., “Extracorporeal shock-wave lithotripsy or ureteroscopy as primary treatment for ureteric stones: a retrospective study comparing two different treatment strategies,” Scandinavian journal of urology and nephrology 40.2 (2006): 113-118.
[8]Antonio Corrêa, Lopes Neto et al., “Prospective randomized study of treatment of large proximal ureteral stones: extracorporeal shock wave lithotripsy versus ureterolithotripsy versus laparoscopy,” The Journal of urology 187.1 (2012): 164-168.
[9]Alfredo Odón, Rodriguez et al., “Computed tomography of kidney stones for extracorporeal shock wave lithotripsy,” 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006.
[10]Rikiya, Yamashita et al., “Convolutional neural networks: an overview and application in radiology,” Insights into imaging 9.4 (2018): 611-629.
[11]Martin, Längkvist et al., “Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks,” Computers in biology and medicine 97 (2018): 153-160.
[12]Anushri, Parakh et al., “Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization,” Radiology. Artificial intelligence 1.4 (2019).
[13]Olaf, Ronneberger Philipp Fischer, and Thomas Brox., “U-net: Convolutional networks for biomedical image segmentation,” International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
[14]Wongsakorn, Preedanan et al., “Urinary Stones Segmentation in Abdominal X-Ray Images Based on U-Net Deep Learning Model and Data Augmentation Techniques,” 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). IEEE, 2020.
[15]Soumya, Gupta et al., “Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy,” arXiv preprint arXiv:2104.01268 (2021).
[16]Soumya, Gupta et al., “Mi-unet: Improved segmentation in ureteroscopy,” 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020.
[17]Sergey, Ioffe and Christian Szegedy., “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” International conference on machine learning. PMLR, 2015.
[18]Md Zahangir, Alom et al., “Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,” arXiv preprint arXiv:1802.06955 (2018) .
[19]https://github.com/Yonsei-TAIL/Urinary-Stone-Challenge
[20]Yingying, Xu et al., “PA‐ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images,” Medical Physics 48.7 (2021): 3752-3766.
[21]Kaiming, He et al., “Deep residual learning for image recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[22]Ozan, Oktay et al. , “Attention u-net: Learning where to look for the pancreas,” arXiv preprint arXiv:1804.03999 (2018).
[23]Edwin, Thomas et al., “Multi-res-attention UNet: a CNN model for the segmentation of focal cortical dysplasia lesions from magnetic resonance images,” IEEE Journal of Biomedical and Health Informatics 25.5 (2020): 1724-1734.
[24]Changlu, Guo et al., “Sa-unet: Spatial attention u-net for retinal vessel segmentation,” 2020 25th international conference on pattern recognition (ICPR). IEEE, 2021.
[25]Zongwei, Zhou et al., “Unet++: A nested u-net architecture for medical image segmentation,” Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Cham, 2018. 3-11.
[26]Ruchika, Arora Indu Saini, and Neetu Sood., “Modified UNet++ Model: A Deep Model for Automatic Segmentation of Lungs from Chest X-ray Images,” 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC). IEEE, 2021.
[27]Chen, Li et al., “Attention unet++: A nested attention-aware u-net for liver ct image segmentation,” 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020.
[28]Huimin, Huang et al., “Unet 3+: A full-scale connected unet for medical image segmentation,” ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.
[29]Ionut Cosmin, Duta et al., “Improved residual networks for image and video recognition,” 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021.
[30]Saurabh, Singh and Shankar Krishnan., “Filter response normalization layer: Eliminating batch dependence in the training of deep neural networks,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
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