TY - JOUR
T1 - Machine Learning and Deep Learning in Oncologic Imaging
T2 - Potential Hurdles, Opportunities for Improvement, and Solutions - Abdominal Imagers' Perspective
AU - Yedururi, Sireesha
AU - Morani, Ajaykumar C.
AU - Katabathina, Venkata Subbiah
AU - Jo, Nahyun
AU - Rachamallu, Medhini
AU - Prasad, Srinivasa
AU - Marcal, Leonardo
N1 - Publisher Copyright:
© Wolters Kluwer Health, Inc. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - machine learning
KW - oncologic imaging
KW - standard annotations
KW - universal annotations
UR - http://www.scopus.com/inward/record.url?scp=85121969911&partnerID=8YFLogxK
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U2 - 10.1097/RCT.0000000000001183
DO - 10.1097/RCT.0000000000001183
M3 - Review article
C2 - 34270486
AN - SCOPUS:85121969911
SN - 0363-8715
VL - 45
SP - 805
EP - 811
JO - Journal of Computer Assisted Tomography
JF - Journal of Computer Assisted Tomography
IS - 6
ER -