Caveats in Generating Medical Imaging Labels from Radiology Reports

Supervised learning from multiple experts: whom to trust when everyone lies a bit

Vikas C Raykar, Shipeng Yu, Linda H Zhao, Anna Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, and Linda Moy · 2009

Earlier work this paper cites.

Natural language processing techniques for extracting and categorizing finding measurements in narrative radiology reports

M Sevenster, J Buurman, P Liu, JF Peters, and PJ Chang · 2015

Earlier work this paper cites.

Information extraction from multi-institutional radiology reports

Saeed Hassanpour and Curtis P Langlotz · 2016

Earlier work this paper cites.

Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases

Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers · 2017

Cited alongside, same era.

Negbio: a high-performance tool for negation and uncertainty detection in radiology reports

Yifan Peng, Xiaosong Wang, Le Lu, Mohammadhadi Bagheri, Ronald Summers, and Zhiyong Lu · 2018

Cited alongside, same era.

Natural language–based machine learning models for the annotation of clinical radiology reports

John Zech, Margaret Pain, Joseph Titano, Marcus Badgeley, Javin Schefflein, Andres Su, Anthony Costa, Joshua Bederson, Joseph Lehar, and Eric Karl Oermann · 2018

Cited alongside, same era.

Padchest: A large chest x-ray image dataset with multi-label annotated reports

Original

Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, and Maria de la Iglesia-Vayá · 2019

Closest in time.

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

Closest in time.