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https://hdl.handle.net/2440/129003
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dc.contributor.author | Maicas, G. | - |
dc.contributor.author | Nguyen, C. | - |
dc.contributor.author | Motlagh, F. | - |
dc.contributor.author | Nascimento, J.C. | - |
dc.contributor.author | Carneiro, G. | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2020, vol.2020-April, pp.1339-1342 | - |
dc.identifier.isbn | 9781538693308 | - |
dc.identifier.issn | 1945-7928 | - |
dc.identifier.issn | 1945-8452 | - |
dc.identifier.uri | http://hdl.handle.net/2440/129003 | - |
dc.description.abstract | Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with small training sets). However, the effectiveness of meta-training relies on the availability of a reasonable number of hand-designed classification tasks, which are costly to obtain, and consequently rarely available. In this paper, we propose a new method to unsupervisedly design a large number of classification tasks to meta-train medical image classifiers. We evaluate our method on a breast dynamically contrast enhanced magnetic resonance imaging (DCE-MRI) data set that has been used to benchmark few-shot training methods of medical image classifiers. Our results show that the proposed unsupervised task design to meta-train medical image classifiers builds a pre-trained model that, after fine-tuning, produces better classification results than other unsupervised and supervised pre-training methods, and competitive results with respect to meta-training that relies on hand-designed classification tasks. | - |
dc.description.statementofresponsibility | Gabriel Maicasy, Cuong Nguyeny, Farbod Motlaghy, Jacinto C. Nascimentozz, Gustavo Carneiro | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE International Symposium on Biomedical Imaging | - |
dc.rights | ©2020 IEEE | - |
dc.source.uri | https://ieeexplore.ieee.org/xpl/conhome/9091448/proceeding | - |
dc.subject | meta-training; unsupervised learning; unsupervised task design; breast image analysis; magnetic resonance imaging; few-shot classification; pre-training; clustering | - |
dc.title | Unsupervised task design to meta-train medical image classifiers | - |
dc.type | Conference paper | - |
dc.contributor.conference | IEEE International Symposium on Biomedical Imaging (ISBI) (3 Apr 2020 - 7 Apr 2020 : Iowa City, Iowa, USA) | - |
dc.identifier.doi | 10.1109/ISBI45749.2020.9098470 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP180103232 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Nguyen, C. [0000-0003-2672-6291] | - |
dc.identifier.orcid | Carneiro, G. [0000-0002-5571-6220] | - |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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