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Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide.

Shelly Soffer1, Avi Ben-Cohen1, Orit Shimon1

  • 1From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.).

Radiology
|January 30, 2019
PubMed
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Deep learning, particularly convolutional neural networks, shows promise in analyzing radiologic images across major organs. This guide aids radiologists in research using these advanced AI techniques.

Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Medical Image Analysis

Background:

  • Deep learning (DL) has seen rapid advancements, gaining significant traction in the radiology field.
  • Radiologic imaging analysis is increasingly benefiting from AI-driven technologies.

Purpose of the Study:

  • To introduce deep learning technology and its application in radiology research.
  • To present the design process for deep learning radiology studies.
  • To survey the use of convolutional neural networks (CNNs) in radiologic imaging.

Main Methods:

  • A comprehensive survey of existing literature on deep learning applications in radiology.
  • Focus on studies utilizing convolutional neural networks (CNNs).
  • Analysis of CNN applications across five major organ systems: chest, breast, brain, musculoskeletal, and abdomen/pelvis.

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Main Results:

  • Deep learning, especially CNNs, is being actively applied to radiologic image analysis.
  • Significant research exists for chest, breast, brain, musculoskeletal, and abdominal/pelvic imaging.
  • The survey highlights the growing integration of AI in diagnostic radiology.

Conclusions:

  • Deep learning, particularly CNNs, offers powerful tools for radiologic image analysis.
  • Current challenges and future trends indicate continued AI evolution in radiology.
  • This article serves as a foundational guide for radiologists venturing into AI-powered research.