WFRF:(Lundström Karl)
Sökning: WFRF:(Lundström Karl) > Training nuclei det...
- 44 av 81
- Föregående post
- Nästa post
- Till träfflistan
Training nuclei detection algorithms with simple annotations
-
- Kost, Henning (författare)
- Fraunhofer-Institut fur Bildgestutzte Medizin MEVIS,Fraunhofer Mevis
-
- Homeyer, André (författare)
- Fraunhofer-Institut fur Bildgestutzte Medizin MEVIS,Fraunhofer Mevis
-
- Molin, Jesper, 1987 (författare)
- Sectra AB,Chalmers tekniska högskola,Chalmers University of Technology
- visa fler...
- visa färre...
- (creator_code:org_t)
- Elsevier BV, 2017
- 2017
- Engelska.
- Ingår i: Journal of Pathology Informatics. - : Elsevier BV. - 2229-5089 .- 2153-3539. ; 8:1
- Tidskriftsartikel (refereegranskat)
Abstract
Ämnesord
Stäng
- Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- Active learning
- Machine learning
- Nuclei detection
- Training set generation
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
- Journal of Pathology Informatics (Sök värdpublikationen i LIBRIS)
Till lärosätets databas
- 44 av 81
- Föregående post
- Nästa post
- Till träfflistan
Hitta mer i SwePub
- Av författaren/redakt...
- Kost, Henning
- Homeyer, André
- Molin, Jesper, 1 ...
- Lundström, Claes ...
- Hahn, Horst Karl
- Om ämnet
-
- TEKNIK OCH TEKNOLOGIER
- TEKNIK OCH TEKNO ...
- och Medicinteknik
-
- TEKNIK OCH TEKNOLOGIER
- TEKNIK OCH TEKNO ...
- och Elektroteknik oc ...
- och Signalbehandling
- Artiklar i publikationen
- Journal of Patho ...
- Av lärosätet
- Chalmers tekniska högskola
- Linköpings universitet
Sök utanför SwePub
- Sök vidare i:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - Nationella bibliotekssystem
- LIBRIS.kb.se
