Fine-grained spatial information extraction in radiology as two-turn question answering

Author:

Datta Surabhi,Roberts Kirk

Funder

National Institute of Biomedical Imaging and Bioengineering

Patient-Centered Outcomes Research Institute

Publisher

Elsevier BV

Subject

Health Informatics

Reference29 articles.

1. T. Syeda-Mahmood, P. D, K.C.L. Wong, P. D, J.T. Wu, M.D., M.P. H, A. Jadhav, P. D, O. Boyko, M.D.P. D, Extracting and Learning Fine-Grained Labels from Chest Radiographs, arXiv:2011.09517 [cs]arXiv:2011.09517. URL http://arxiv.org/abs/2011.09517.

2. Extracting clinical terms from radiology reports with deep learning;Sugimoto;J. Biomed. Inform.,2021

3. Toward Complete Structured Information Extraction from Radiology Reports Using Machine Learning;Steinkamp;J. Digit. Imaging,2019

4. S. Datta, M. Ulinski, J. Godfrey-Stovall, S. Khanpara, R.F. Riascos-Castaneda, K. Roberts, Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports, in: Proceedings of the 12th Language Resources and Evaluation Conference, 2020, pp. 2251–2260. URL https://www.aclweb.org/anthology/2020.lrec-1.274.

5. Automatic image classification using labels from radiology text reports: Predicting Deauville scores;Bradshaw;J. Nucl. Med.,2020

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