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...

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Bibliographic Details
Published in:	IEEE transactions on medical imaging Vol. 37; no. 1; pp. 316 - 325
Main Authors:	Mercan, Caner, Aksoy, Selim, Mercan, Ezgi, Shapiro, Linda G., Weaver, Donald L., Elmore, Joann G.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
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