Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis

In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neur...

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Bibliographic Details
Published in:Frontiers in oncology Vol. 11; p. 638182
Main Authors: Yang, Ruixin, Yu, Yingyan
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 09.03.2021
Subjects:
analysis
convolutional neural network
medical images
object detection
Oncology
semantic segmentation
medical images
object detection
semantic segmentation
analysis
convolutional neural network
ISSN:2234-943X, 2234-943X
Online Access:Get full text
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Summary:In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases.
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This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology
Edited by: Youyong Kong, Southeast University, China
Reviewed by: Guanzhen Yu, Shanghai University of Traditional Chinese Medicine, China; Weiming Mi, Tsinghua University, China
ISSN:2234-943X
2234-943X
DOI:10.3389/fonc.2021.638182