Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets: Reducing the need of labeled data on biological datasets

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dc.contributor.authorMedela, Alfonso
dc.contributor.authorPicon, Artzai
dc.contributor.authorSaratxaga, Cristina L.
dc.contributor.authorBelar, Oihana
dc.contributor.authorCabezon, Virginia
dc.contributor.authorCicchi, Riccardo
dc.contributor.authorBilbao, Roberto
dc.contributor.authorGlover, Ben
dc.contributor.institutionCOMPUTER_VISION
dc.contributor.institutionVISUAL
dc.date.issued2019-07-11
dc.descriptionPublisher Copyright: © 2019 IEEE.
dc.description.abstractAlthough deep learning pathology diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, they still require a huge amount of well annotated data for training. Generating such extensive and well labelled datasets is time consuming and is not feasible for certain tasks and so, most of the medical datasets available are scarce in images and therefore, not enough for training. In this work we validate that the use of few shot learning techniques can transfer knowledge from a well defined source domain from Colon tissue into a more generic domain composed by Colon, Lung and Breast tissue by using very few training images. Our results show that our few-shot approach is able to obtain a balanced accuracy (BAC) of 90% with just 60 training images, even for the Lung and Breast tissues that were not present on the training set. This outperforms the finetune transfer learning approach that obtains 73% BAC with 60 images and requires 600 images to get up to 81% BAC.en
dc.description.statusPeer reviewed
dc.format.extent5
dc.format.extent340424
dc.identifier.citationMedela , A , Picon , A , Saratxaga , C L , Belar , O , Cabezon , V , Cicchi , R , Bilbao , R & Glover , B 2019 , Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets : Reducing the need of labeled data on biological datasets . in unknown . 1945-7928 , IEEE , pp. 1860-1864 , 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 , Venice , Italy , 8/04/19 . https://doi.org/10.1109/isbi.2019.8759182
dc.identifier.citationconference
dc.identifier.doi10.1109/isbi.2019.8759182
dc.identifier.isbn978-1-5386-3642-8
dc.identifier.isbn978-1-5386-3641-1
dc.identifier.isbn9781538636411
dc.identifier.otherresearchoutputwizard: 11556/769
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85073902001&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofunknown
dc.relation.ispartofseries1945-7928
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsHistopathology analysis
dc.subject.keywordsFew shot learning
dc.subject.keywordsConvolutional neural network
dc.subject.keywordsDomain adaptation
dc.subject.keywordsOptical biopsy
dc.subject.keywordsHistopathology analysis
dc.subject.keywordsFew shot learning
dc.subject.keywordsConvolutional neural network
dc.subject.keywordsDomain adaptation
dc.subject.keywordsOptical biopsy
dc.subject.keywordsBiomedical Engineering
dc.subject.keywordsRadiology, Nuclear Medicine and Imaging
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision support/PICCOLO
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision support/PICCOLO
dc.subject.keywordsFunding Info
dc.subject.keywordsThis study has received funding from the European_x000D_ Union’s Horizon 2020 research and innovation programme_x000D_ under grant agreement No. 732111 (PICCOLO project).
dc.subject.keywordsThis study has received funding from the European_x000D_ Union’s Horizon 2020 research and innovation programme_x000D_ under grant agreement No. 732111 (PICCOLO project).
dc.titleFew Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets: Reducing the need of labeled data on biological datasetsen
dc.typeconference output
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