Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/121448
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Type: Conference paper
Title: Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI
Author: Maicas, G.
Snaauw, G.
Bradley, A.P.
Reid, I.
Carneiro, G.
Citation: Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019 / vol.2019-April, pp.1057-1060
Publisher: IEEE
Issue Date: 2019
Series/Report no.: IEEE International Symposium on Biomedical Imaging
ISBN: 9781538636411
ISSN: 1945-7928
1945-8452
Conference Name: IEEE International Symposium on Biomedical Imaging (ISBI) (08 Apr 2019 - 11 Apr 2019 : Venice, ITALY)
Statement of
Responsibility: 
Gabriel Maicas, Gerard Snaauw, Andrew P. Bradley, Ian Reid, Gustavo Carneiro
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.
Keywords: Saliency; weakly supervised detection; model interpretability; diagnosis explanation; breast lesion localization; breast magnetic resonance imaging
Rights: © 2019 IEEE
RMID: 1000000450
DOI: 10.1109/ISBI.2019.8759402
Grant ID: http://purl.org/au-research/grants/arc/DP180103232
http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/FL130100102
Appears in Collections:Computer Science publications

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