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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Vydané v:IEEE transactions on medical imaging Ročník 37; číslo 1; s. 316 - 325
Hlavní autori: Mercan, Caner, Aksoy, Selim, Mercan, Ezgi, Shapiro, Linda G., Weaver, Donald L., Elmore, Joann G.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Popis
Shrnutí: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, and the need for identifying regions that belong to multiple classes with different clinical significances have emerged as two new challenges. However, generalizability of the state-of-the-art algorithms, whose accuracies were reported on carefully selected regions of interest (ROIs) for the binary benign versus cancer classification, to these multi-class learning and localization problems is currently unknown. This paper presents our potential solutions to these challenges by exploiting the viewing records of pathologists and their slide-level annotations in weakly supervised learning scenarios. First, we extract candidate ROIs from the logs of pathologists' image screenings based on different behaviors, such as zooming, panning, and fixation. Then, we model each slide with a bag of instances represented by the candidate ROIs and a set of class labels extracted from the pathology forms. Finally, we use four different multi-instance multi-label learning algorithms for both slide-level and ROI-level predictions of diagnostic categories in whole slide breast histopathology images. Slide-level evaluation using 5-class and 14-class settings showed average precision values up to 81% and 69%, respectively, under different weakly labeled learning scenarios. ROI-level predictions showed that the classifier could successfully perform multi-class localization and classification within whole slide images that were selected to include the full range of challenging diagnostic categories.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2017.2758580