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Several deep learning architectures have been proposed over the last years to deal with the problem of generating a written report given an imaging exam as input.
“Bleu: a Method for Automatic Evaluation of Machine Translation”
Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu · 2002
Earlier work this paper cites.
“ROUGE: A Package for Automatic Evaluation of Summaries”
Chin-Yew Lin · 2004
Earlier work this paper cites.
“Preparing a collection of radiology examinations for distribution and retrieval”
Dina Demner-Fushman et al · 2015
Earlier work this paper cites.
“Cider: Consensus-based image description evaluation”
Ramakrishna Vedantam, C Lawrence and Devi Parikh · 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.
“Mobilenets: Efficient convolutional neural networks for mobile vision applications”
Andrew Howard et al · 2017
Earlier work this paper cites.
“Densely connected convolutional networks”
Gao Huang, Zhuang Liu, Laurens Van and Kilian Weinberger · 2017
Cited alongside, same era.
“A Diagnostic Report Generator from CT Volumes on Liver Tumor with Semi-supervised Attention Mechanism”
Jiang Tian, Cong Li, Zhongchao Shi and Feiyu Xu · 2018
Cited alongside, same era.
“Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays”
Xiaosong Wang et al · 2018
Cited alongside, same era.
“Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison”
Jeremy Irvin et al · 2019
Cited alongside, same era.
“Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation”
Christy Li, Xiaodan Liang, Zhiting Hu and Eric Xing · 2019
Cited alongside, same era.
“Clinically Accurate Chest X-Ray Report Generation”
“Improved Disease Classification in Chest X-Rays with Transferred Features from Report Generation”
Yuan Xue and Xiaolei Huang · 2019
Later among the works it cites.
“Baselines for Chest X-Ray Report Generation”
William Boag et al · 2020
Closest in time.
“Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics”
Nitika Mathur, Timothy Baldwin and Trevor Cohn · 2020
Closest in time.
“When Radiology Report Generation Meets Knowledge Graph”
Yixiao Zhang et al · 2020
Closest in time.
“Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports”
Yuhao Zhang et al · 2020
Closest in time.
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Guanxiong Liu et al · 2019
Cited alongside, same era.