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

Sireesha Yedururi, Ajaykumar C. Morani, Venkata Subbiah Katabathina, Nahyun Jo, Medhini Rachamallu, Srinivasa Prasad, Leonardo Marcal

Producción científica: Review articlerevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalEnglish (US)
Páginas (desde-hasta)805-811
Número de páginas7
PublicaciónJournal of Computer Assisted Tomography
Volumen45
N.º6
DOI
EstadoPublished - 2021

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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