Machine learning approaches in medical image analysis: from detection to diagnosis

Publikation: Bidrag til tidsskrift › Leder › Forskning › fagfællebedømt

Standard

Machine learning approaches in medical image analysis : from detection to diagnosis. / de Bruijne, Marleen.

I: Medical Image Analysis, Bind 33, 2016, s. 94-97.

Publikation: Bidrag til tidsskrift › Leder › Forskning › fagfællebedømt

Harvard

de Bruijne, M 2016, 'Machine learning approaches in medical image analysis: from detection to diagnosis', Medical Image Analysis, bind 33, s. 94-97. https://doi.org/10.1016/j.media.2016.06.032

APA

de Bruijne, M. (2016). Machine learning approaches in medical image analysis: from detection to diagnosis. Medical Image Analysis, 33, 94-97. https://doi.org/10.1016/j.media.2016.06.032

Vancouver

de Bruijne M. Machine learning approaches in medical image analysis: from detection to diagnosis. Medical Image Analysis. 2016;33:94-97. https://doi.org/10.1016/j.media.2016.06.032

Author

de Bruijne, Marleen. / Machine learning approaches in medical image analysis : from detection to diagnosis. I: Medical Image Analysis. 2016 ; Bind 33. s. 94-97.

Bibtex

@article{71e65e2cea704d4b980aa5de7ec69285,
title = "Machine learning approaches in medical image analysis: from detection to diagnosis",
abstract = "Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.",
keywords = "Classification, Computer aided diagnosis, Machine learning, Transfer learning",
author = "{de Bruijne}, Marleen",
year = "2016",
doi = "10.1016/j.media.2016.06.032",
language = "English",
volume = "33",
pages = "94--97",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Machine learning approaches in medical image analysis

T2 - from detection to diagnosis

AU - de Bruijne, Marleen

PY - 2016

Y1 - 2016

N2 - Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.

AB - Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.

KW - Classification

KW - Computer aided diagnosis

KW - Machine learning

KW - Transfer learning

UR - http://www.scopus.com/inward/record.url?scp=84982105864&partnerID=8YFLogxK

U2 - 10.1016/j.media.2016.06.032

DO - 10.1016/j.media.2016.06.032

M3 - Editorial

C2 - 27481324

AN - SCOPUS:84982105864

VL - 33

SP - 94

EP - 97

JO - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

ER -

ID: 167098898