Fetching the paper…
Reading the bibliography…
Recently, deep learning has produced encouraging results for kidney stone classification using endoscope images.
The evolution of lasers in urology
Amir Zarrabi and Andreas J Gross · 2011
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Trends in urological stone disease: a 5-year update of hospital episode statistics
Hendrik Heers and Benjamin W Turney · 2016
Earlier work this paper cites.
Use of the moses technology to improve holmium laser lithotripsy outcomes: a preclinical study
Mostafa M Elhilali, Shadie Badaan, Ahmed Ibrahim, and Sero Andonian · 2017
Earlier work this paper cites.
mystone: A system for automatic kidney stone classification
Joan Serrat, Felipe Lumbreras, Francisco Blanco, Manuel Valiente, and Montserrat López-Mesas · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Advances in lasers for the treatment of stones—a systematic review
Peter Kronenberg and Bhaskar Somani · 2018
Earlier work this paper cites.
Metric learning for kidney stone classification
Alejandro Torrell Amado · 2018
Earlier work this paper cites.
Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion
Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Tanzila Saba, Kashif Javed, Ikram Ullah Lali, Urcun John Tanik, and Amjad Rehman · 2019
Cited alongside, same era.
Deep learning computer vision algorithm for detecting kidney stone composition
Kristian M Black, Hei Law, Ali Aldoukhi, Jia Deng, and Khurshid R Ghani · 2020
Cited alongside, same era.
MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark
MMSegmentation Contributors · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Double-blinded prospective randomized clinical trial comparing regular and moses modes of holmium laser lithotripsy
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Yuanfeng Ji, Haotian Bai, Jie Yang, Chongjian Ge, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xiang Wan, et al · 2022
Later among the works it cites.
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
Later among the works it cites.
Automated detection of kidney stone using deep learning models
B Manoj, Neethu Mohan, Sachin Kumar, et al · 2022
Later among the works it cites.
On the in vivo recognition of kidney stones using machine learning
Gilberto Ochoa-Ruiz, Vincent Estrade, Francisco Lopez, Daniel Flores-Araiza, Jonathan El Beze, Dinh-Hoan Trinh, Miguel Gonzalez-Mendoza, Pascal Eschwège, Jacques Hubert, and Christian Daul · 2022
Later among the works it cites.
Medical image understanding with pretrained vision language models: A comprehensive study
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ahmed Ibrahim, Mostafa M Elhilali, Nader Fahmy, Serge Carrier, and Sero Andonian · 2020
Cited alongside, same era.
Wei Zhu, Haofu Liao, Wenbin Li, Weijian Li, and Jiebo Luo · 2020
Cited alongside, same era.
Trends in the prevalence of kidney stones in the united states from 2007 to 2016
Api Chewcharat and Gary Curhan · 2021
Cited alongside, same era.
Visual prompting: Modifying pixel space to adapt pre-trained models
Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola · 2022
Cited alongside, same era.
Towards automatic recognition of pure and mixed stones using intra-operative endoscopic digital images
Vincent Estrade, Michel Daudon, Emmanuel Richard, Jean-christophe Bernhard, Franck Bladou, Grégoire Robert, and Baudouin Denis de Senneville · 2022
Cited alongside, same era.
Skincon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis
Roxana Daneshjou, Mert Yuksekgonul, Zhuo Ran Cai, Roberto A Novoa, and James Zou
Cited in the paper.
Ziyuan Qin, Huahui Yi, Qicheng Lao, and Kang Li · 2022
Later among the works it cites.
S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning
Yabin Wang, Zhiwu Huang, and Xiaopeng Hong · 2022
Later among the works it cites.
Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
Later among the works it cites.
Unified vision and language prompt learning
Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy · 2022
Later among the works it cites.
Yuanhan Zhang, Kaiyang Zhou, and Ziwei Liu · 2022
Later among the works it cites.