Literature DB >> 33768000

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

Ruixin Yang1, Yingyan Yu1.   

Abstract

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.
Copyright © 2021 Yang and Yu.

Entities:  

Keywords:  analysis; convolutional neural network; medical images; object detection; semantic segmentation

Year:  2021        PMID: 33768000      PMCID: PMC7986719          DOI: 10.3389/fonc.2021.638182

Source DB:  PubMed          Journal:  Front Oncol        ISSN: 2234-943X            Impact factor:   6.244


  8 in total

Review 1.  Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities.

Authors:  Navid Hasani; Faraz Farhadi; Michael A Morris; Moozhan Nikpanah; Arman Rhamim; Yanji Xu; Anne Pariser; Michael T Collins; Ronald M Summers; Elizabeth Jones; Eliot Siegel; Babak Saboury
Journal:  PET Clin       Date:  2022-01

2.  Deep Learning Approach to Automatize TMTV Calculations Regardless of Segmentation Methodology for Major FDG-Avid Lymphomas.

Authors:  Wendy Revailler; Anne Ségolène Cottereau; Cedric Rossi; Rudy Noyelle; Thomas Trouillard; Franck Morschhauser; Olivier Casasnovas; Catherine Thieblemont; Steven Le Gouill; Marc André; Herve Ghesquieres; Romain Ricci; Michel Meignan; Salim Kanoun
Journal:  Diagnostics (Basel)       Date:  2022-02-06

Review 3.  A comprehensive review of methods based on deep learning for diabetes-related foot ulcers.

Authors:  Jianglin Zhang; Yue Qiu; Li Peng; Qiuhong Zhou; Zheng Wang; Min Qi
Journal:  Front Endocrinol (Lausanne)       Date:  2022-08-08       Impact factor: 6.055

4.  Prediction model for suicide based on back propagation neural network and multilayer perceptron.

Authors:  Juncheng Lyu; Hong Shi; Jie Zhang; Jill Norvilitis
Journal:  Front Neuroinform       Date:  2022-08-11       Impact factor: 3.739

5.  A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks.

Authors:  Chengzhi Yang; Hongjun Guo
Journal:  Comput Intell Neurosci       Date:  2022-09-06

6.  Scene Classification in the Environmental Art Design by Using the Lightweight Deep Learning Model under the Background of Big Data.

Authors:  Lu Liu
Journal:  Comput Intell Neurosci       Date:  2022-06-13

7.  Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network.

Authors:  Ho-Kyung Lim; Seok-Ki Jung; Seung-Hyun Kim; Yongwon Cho; In-Seok Song
Journal:  BMC Oral Health       Date:  2021-12-07       Impact factor: 2.757

Review 8.  Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.

Authors:  Maxwell A Konnaris; Matthew Brendel; Mark Alan Fontana; Miguel Otero; Lionel B Ivashkiv; Fei Wang; Richard D Bell
Journal:  Arthritis Res Ther       Date:  2022-03-11       Impact factor: 5.156

  8 in total

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