Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/121448
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Maicas Suso, G. | - |
dc.contributor.author | Snaauw, G. | - |
dc.contributor.author | Bradley, A.P. | - |
dc.contributor.author | Reid, I. | - |
dc.contributor.author | Carneiro, G. | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2019, vol.2019-April, pp.1057-1060 | - |
dc.identifier.isbn | 9781538636411 | - |
dc.identifier.issn | 1945-7928 | - |
dc.identifier.issn | 1945-8452 | - |
dc.identifier.uri | http://hdl.handle.net/2440/121448 | - |
dc.description.abstract | There is a heated debate on how to interpret the decisions provided by deep learning models (DLM), where the main approaches rely on the visualization of salient regions to interpret the DLM classification process. However, these approaches generally fail to satisfy three conditions for the problem of lesion detection from medical images: 1) for images with lesions, all salient regions should represent lesions, 2) for images containing no lesions, no salient region should be produced, and 3) lesions are generally small with relatively smooth borders. We propose a new model-agnostic paradigm to interpret DLM classification decisions supported by a novel definition of saliency that incorporates the conditions above. Our model-agnostic 1-class saliency detector (MASD) is tested on weakly supervised breast lesion detection from DCE-MRI, achieving state-of-the-art detection accuracy when compared to current visualization methods. | - |
dc.description.statementofresponsibility | Gabriel Maicas, Gerard Snaauw, Andrew P. Bradley, Ian Reid, Gustavo Carneiro | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE International Symposium on Biomedical Imaging | - |
dc.rights | © 2019 IEEE | - |
dc.source.uri | http://dx.doi.org/10.1109/isbi.2019.8759402 | - |
dc.subject | Saliency; weakly supervised detection; model interpretability; diagnosis explanation; breast lesion localization; breast magnetic resonance imaging | - |
dc.title | Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI | - |
dc.type | Conference paper | - |
dc.contributor.conference | IEEE International Symposium on Biomedical Imaging (ISBI) (8 Apr 2019 - 11 Apr 2019 : Venice, ITALY) | - |
dc.identifier.doi | 10.1109/ISBI.2019.8759402 | - |
dc.publisher.place | online | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP180103232 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/CE140100016 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FL130100102 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Maicas Suso, G. [0000-0002-0490-7633] | - |
dc.identifier.orcid | Snaauw, G. [0000-0002-0011-0272] | - |
dc.identifier.orcid | Reid, I. [0000-0001-7790-6423] | - |
dc.identifier.orcid | Carneiro, G. [0000-0002-5571-6220] | - |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.