Automated Radiology Report Summarization Using an Open-Source Natural Language Processing Pipeline

By D.J Goff, T. Loehfelm. 2018

41 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yuhao Zhang
2. Curtis P. Langlotz
3. Daisy Yi Ding
4. Tianpei Qian
5. Arvid Lundervold
6. Christopher D. Manning
7. Percy Liang
8. Mohit Bansal

In Molecular Biology

1. Christopher D. Manning
2. Arvid Lundervold
3. Curtis P. Langlotz
4. Yuhao Zhang
5. Chris Dyer
6. Yoav Goldberg
7. David Weiss
8. Noah A. Smith

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Curtis P. Langlotz
3. Florian Tramèr
4. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “Recently, deep learning methods have also been used for automated generation of radiology reports, typically incorporating long-short-term-memory (LSTM) network models to generate the textual paragraphs [314, 315, 316, 317], and also to identify findings in radiology reports [318, 319, 320].”
• Learning to Summarize Radiology Findings (2018, background): “Goff and Loehfelm (2018) built an NLP pipeline to identify asserted and negated disease entities in the impression section of radiology reports as a step towards report summarization.”