Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images

Digital pathology has entered a new era with the availability of whole slide scanners that create the high-resolution images of full biopsy slides. Consequently, the uncertainty regarding the correspondence between the image areas and the diagnostic labels assigned by pathologists at the slide level...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on medical imaging Jg. 37; H. 1; S. 316 - 325
Hauptverfasser:	Mercan, Caner, Aksoy, Selim, Mercan, Ezgi, Shapiro, Linda G., Weaver, Donald L., Elmore, Joann G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Annotations Biopsy Breast Breast - diagnostic imaging breast histopathology Breast Neoplasms - classification Breast Neoplasms - diagnostic imaging Cancer Classification Diagnostic systems Digital imaging Digital pathology Electronic mail Histocytochemistry - methods Histopathology Humans Image analysis Image classification Image Interpretation, Computer-Assisted - methods Image resolution Labels Learning algorithms Lesions Localization Machine learning Medical imaging multi-class classification Panning Pathology region of interest detection Scanners State of the art Training weakly-labeled learning whole slide imaging Zooming
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!