|
KMID : 1137820220430040259
|
|
의공학회지
2022 Volume.43 No. 4 p.259 ~ p.270
|
|
|
A review of Explainable AI Techniques in Medical Imaging
|
|
Lee Dong-Eon
Park Chun-Su
Kang Jeong-Woon
Kim Min-Woo
|
|
|
Abstract
|
|
|
|
Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlin- earity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, sci- entific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.
|
|
|
KEYWORD
|
|
|
Explainable AI, XAI, Medical imaging, Deep learning
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
 
|
|