Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective.

Journal: Journal of computer assisted tomography
PMID: 34270486

Abstract

The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imaging, including the lack of availability of a large number of annotated data sets and lack of use of consistent methodology and terminology for reporting the findings observed on the staging and follow-up imaging studies that apply to a wide spectrum of solid tumors. This short review discusses some potential hurdles to the implementation of machine learning in oncologic imaging, opportunities for improvement, and potential solutions that can facilitate robust machine learning from the vast number of radiology reports and annotations generated by the dictating radiologists.

Authors

  • Sireesha Yedururi
    From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.
  • Ajaykumar C Morani
    Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009.
  • Venkata Subbiah Katabathina
    Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX.
  • Nahyun Jo
    Department of Internal Medicine, UAB Montgomery Regional Medical Campus, Montogomery, AL.
  • Medhini Rachamallu
    Department of Biomedical Engineering, The University of Virginia, VA.
  • Srinivasa Prasad
    From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.
  • Leonardo Marcal
    From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston.

Keywords

External Resources

Popular Topics
Recent Journals