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|Title:||Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI|
|Citation:||Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019 / vol.2019-April, pp.1057-1060|
|Series/Report no.:||IEEE International Symposium on Biomedical Imaging|
|Conference Name:||IEEE International Symposium on Biomedical Imaging (ISBI) (08 Apr 2019 - 11 Apr 2019 : Venice, ITALY)|
|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|
|Appears in Collections:||Computer Science publications|
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