Caveats in Generating Medical Imaging Labels from Radiology Reports

Caveats in Generating Medical Imaging Labels from Radiology Reports

6 May 2019

Alexander Rhodes

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Papers citing "Caveats in Generating Medical Imaging Labels from Radiology Reports"

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Title
Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs Tae Soo Kim Geonwoon Jang Sanghyup Lee Thijs Kooi 77 7 0 30 Sep 2022
Improving Classification Model Performance on Chest X-Rays through Lung Segmentation Hilda Azimi Jianxing Zhang Pengcheng Xi H. Asad Ashkan Ebadi Stéphane Tremblay Alexander Wong 85 8 0 22 Feb 2022
Deep Learning for Chest X-ray Analysis: A Survey Ecem Sogancioglu E. Çallı Bram van Ginneken K. G. V. Leeuwen K. Murphy LM&MA 124 329 0 15 Mar 2021
VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels Saahil Jain Akshay Smit Steven QH Truong C. Nguyen Minh-Thanh Huynh Mudit Jain Victoria A Young A. Ng M. Lungren Pranav Rajpurkar MedIm 62 31 0 23 Feb 2021
Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice V. Kulkarni M. Gawali A. Kharat VLM 108 21 0 03 Feb 2021
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly T. Gupte Mrunmai Niljikar M. Gawali V. Kulkarni A. Kharat Aniruddha Pant 24 16 0 19 Jan 2021
Labelling imaging datasets on the basis of neuroradiology reports: a validation study D. Wood S. Kafiabadi A. A. Busaidi Emily Guilhem J. Lynch ... M. Benger Gareth J. Barker S. Ourselin James H. Cole Thomas C Booth 29 15 0 08 Jul 2020
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders Nina Shvetsova Bart Bakker Irina Fedulova H. Schulz Dmitry V. Dylov 90 84 0 23 Jun 2020