Fetching the paper…
Reading the bibliography…
Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation.
Mimic-cxr-jpg, a large publicly available database of labeled chest radiographs
Alistair EW Johnson, Tom J Pollard, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G Mark, Seth J Berkowitz, and Steven Horng. 2019 · 1901
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
Earlier work this paper cites.
Addressing data bias problems for chest x-ray image report generation
Philipp Harzig, Yan-Ying Chen, Francine Chen, and Rainer Lienhart. 2019 · 1908
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Show, describe and conclude: On exploiting the structure information of chest x-ray reports
Baoyu Jing, Zeya Wang, and Eric Xing. 2020 · 2004
Earlier work this paper cites.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Y Ng, and Matthew P Lungren. 2020 · 2004
Earlier work this paper cites.
Generating radiology reports via memory-driven transformer
Zhihong Chen, Yan Song, Tsung-Hui Chang, and Xiang Wan. 2020b · 2010
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen. 2010 · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
Earlier work this paper cites.
Learning visual-semantic embeddings for reporting abnormal findings on chest x-rays
Jianmo Ni, Chun-Nan Hsu, Amilcare Gentili, and Julian McAuley. 2020 · 2010
Cited alongside, same era.
Meteor 1.3: Automatic metric for reliable optimization and evaluation of machine translation systems
Michael Denkowski and Alon Lavie. 2011 · 2011
Cited alongside, same era.
Contrastive learning with adversarial perturbations for conditional text generation
Seanie Lee, Dong Bok Lee, and Sung Ju Hwang. 2020 · 2012
Cited alongside, same era.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Cited alongside, same era.
Contrastive unsupervised word alignment with non-local features
Yang Liu and Maosong Sun. 2015 · 2015
Cited alongside, same era.
Large margin neural language model
Jiaji Huang, Yi Li, Wei Ping, and Liang Huang. 2018 · 2018
Later among the works it cites.
Hybrid retrieval-generation reinforced agent for medical image report generation
Yuan Li, Xiaodan Liang, Zhiting Hu, and Eric P Xing. 2018 · 2018
Later among the works it cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Later among the works it cites.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al. 2019 · 2019
Later among the works it cites.
Knowledge-driven encode, retrieve, paraphrase for medical image report generation
Christy Y Li, Xiaodan Liang, Zhiting Hu, and Eric P Xing. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015 · 2015
Cited alongside, same era.
Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
Contrastive learning for image captioning
Bo Dai and Dahua Lin. 2017 · 2017
Cited alongside, same era.
On the automatic generation of medical imaging reports
Baoyu Jing, Pengtao Xie, and Eric Xing. 2017 · 2017
Cited alongside, same era.
Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018 · 2018
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020a
Cited in the paper.
Later among the works it cites.
Clinically accurate chest x-ray report generation
Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, and Marzyeh Ghassemi. 2019 · 2019
Later among the works it cites.
Reducing word omission errors in neural machine translation: A contrastive learning approach
Zonghan Yang, Yong Cheng, Yang Liu, and Maosong Sun. 2019 · 2019
Later among the works it cites.
Baselines for chest x-ray report generation
William Boag, Tzu-Ming Harry Hsu, Matthew McDermott, Gabriela Berner, Emily Alesentzer, and Peter Szolovits. 2020 · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
Later among the works it cites.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015 · 2057
Closest in time.