Training medical image analysis systems like radiologists

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dc.contributor.authorMaicas Suso, G.
dc.contributor.authorBradley, A.
dc.contributor.authorNascimento, J.
dc.contributor.authorReid, I.
dc.contributor.authorCarneiro, G.
dc.contributor.conference21st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2018) (16 Sep 2018 - 20 Sep 2018 : Granada)
dc.contributor.editorFrangi, A.
dc.contributor.editorSchnabel, J.
dc.contributor.editorDavatzikos, C.
dc.contributor.editorAlberola-Lopez, C.
dc.contributor.editorFichtinger, G.
dc.date.issued2018
dc.description.abstractThe training of medical image analysis systems using machine learning approaches follows a common script: collect and annotate a large dataset, train the classifier on the training set, and test it on a hold-out test set. This process bears no direct resemblance with radiologist training, which is based on solving a series of tasks of increasing difficulty, where each task involves the use of significantly smaller datasets than those used in machine learning. In this paper, we propose a novel training approach inspired by how radiologists are trained. In particular, we explore the use of meta-training that models a classifier based on a series of tasks. Tasks are selected using teacher-student curriculum learning, where each task consists of simple classification problems containing small training sets. We hypothesize that our proposed meta-training approach can be used to pre-train medical image analysis models. This hypothesis is tested on the automatic breast screening classification from DCE-MRI trained with weakly labeled datasets. The classification performance achieved by our approach is shown to be the best in the field for that application, compared to state of art baseline approaches: DenseNet, multiple instance learning and multi-task learning.
dc.description.statementofresponsibilityGabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, and Gustavo Carneiro
dc.identifier.citationLecture Notes in Artificial Intelligence, 2018 / Frangi, A., Schnabel, J., Davatzikos, C., Alberola-Lopez, C., Fichtinger, G. (ed./s), vol.11070 LNCS, pp.546-554
dc.identifier.doi10.1007/978-3-030-00928-1_62
dc.identifier.isbn9783030009274
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidMaicas Suso, G. [0000-0002-0490-7633]
dc.identifier.orcidReid, I. [0000-0001-7790-6423]
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]
dc.identifier.urihttp://hdl.handle.net/2440/116529
dc.language.isoen
dc.publisherSpringer
dc.relation.granthttp://purl.org/au-research/grants/arc/DP180103232
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100016
dc.relation.granthttp://purl.org/au-research/grants/arc/FL130100102
dc.relation.ispartofseriesLecture notes in computer science; 11070
dc.rights© Springer Nature Switzerland AG 2018
dc.source.urihttps://doi.org/10.1007/978-3-030-00928-1_62
dc.subjectMeta-learning; curriculum learning; multi-task training; breast image analysis; breast screening; magnetic resonance imaging
dc.titleTraining medical image analysis systems like radiologists
dc.typeConference paper
pubs.publication-statusPublished

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