Knowledge Based Versus Data Based: A Historical Perspective on a Continuum of Methodologies for Medical Image Analysis.

Artificial intelligence Computer vision Deep learning Machine learning Medical image analysis

Journal

Neuroimaging clinics of North America
ISSN: 1557-9867
Titre abrégé: Neuroimaging Clin N Am
Pays: United States
ID NLM: 9211377

Informations de publication

Date de publication:
Nov 2020
Historique:
entrez: 11 10 2020
pubmed: 12 10 2020
medline: 12 8 2021
Statut: ppublish

Résumé

The advent of big data and deep learning algorithms has promoted a major shift toward data-driven methods in medical image analysis recently. However, the medical image analysis field has a long and rich history inclusive of both knowledge-driven and data-driven methodologies. In the present article, we provide a historical review of an illustrative sample of medical image analysis methods and locate them along a knowledge-driven versus data-driven continuum. In doing so, we highlight the historical importance as well as current-day relevance of more traditional, knowledge-based artificial intelligence approaches and their complementarity with fully data-driven techniques such as deep learning.

Identifiants

pubmed: 33038992
pii: S1052-5149(20)30049-6
doi: 10.1016/j.nic.2020.06.002
pii:
doi:

Types de publication

Comparative Study Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

401-415

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Disclosure The authors do not have any conflict of interest to report.

Auteurs

Peter Savadjiev (P)

Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada; School of Computer Science, McGill University, Montreal, Quebec, Canada; Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Diagnostic Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada. Electronic address: peter.savadjiev@mcgill.ca.

Caroline Reinhold (C)

Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Diagnostic Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.

Diego Martin (D)

Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Diagnostic Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.

Reza Forghani (R)

Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Diagnostic Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada; Department of Otolaryngology-Head and Neck Surgery, McGill University, Montreal, Quebec, Canada.

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