Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images

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buir.contributor.authorAksoy, Selim
dc.citation.epage325en_US
dc.citation.issueNumber1en_US
dc.citation.spage316en_US
dc.citation.volumeNumber37en_US
dc.contributor.authorMercan, C.en_US
dc.contributor.authorAksoy, Selimen_US
dc.contributor.authorMercan, E.en_US
dc.contributor.authorShapiro, L. G.en_US
dc.contributor.authorWeaver, D. L.en_US
dc.contributor.authorElmore, J. G.en_US
dc.date.accessioned2019-02-21T16:05:39Z
dc.date.available2019-02-21T16:05:39Z
dc.date.issued2018en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractDigital 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.
dc.description.sponsorshipManuscript received July 3, 2017; accepted September 19, 2017. Date of publication October 2, 2017; date of current version December 29, 2017. The work of C. Mercan and S. Aksoy was supported in part by the Scientific and Technological Research Council of Turkey under Grant 113E602 and in part by the GEBIP Award from the Turkish Academy of Sciences. The work of E. Mercan, L. G. Shapiro, D. L. Weaver, and J. G. Elmore was supported by the National Cancer Institute of the National Institutes of Health under Award R01-CA172343 and Award R01-140560. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Cancer Institute or the National Institutes of Health. (Corresponding author: Selim Aksoy.) C. Mercan and S. Aksoy are with the Department of Computer Engineering, Bilkent University, 06800 Ankara, Turkey (e-mail: caner.mercan@cs.bilkent.edu.tr; saksoy@cs.bilkent.edu.tr).
dc.identifier.doi10.1109/TMI.2017.2758580
dc.identifier.issn0278-0062
dc.identifier.urihttp://hdl.handle.net/11693/50265
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.isversionofhttps://doi.org/10.1109/TMI.2017.2758580
dc.relation.projectFoundation for the National Institutes of Health, FNIH: R01-140560, R01-CA172343 - Bilkent Üniversitesi - Türkiye Bilimler Akademisi, TÜBA - Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 113E602
dc.source.titleIEEE Transactions on Medical Imagingen_US
dc.subjectBreast histopathologyen_US
dc.subjectDigital pathologyen_US
dc.subjectMulti-class classificationen_US
dc.subjectRegion of interest detectionen_US
dc.subjectWeakly-labeled learningen_US
dc.subjectWhole slide imagingen_US
dc.titleMulti-instance multi-label learning for multi-class classification of whole slide breast histopathology imagesen_US
dc.typeArticleen_US

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