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https://hdl.handle.net/2440/122728
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Type: | Journal article |
Title: | Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI |
Author: | Maicas, G. Bradley, A.P. Nascimento, J.C. Reid, I. Carneiro, G. |
Citation: | Medical Image Analysis, 2019; 58:101562-1-101562-14 |
Publisher: | Elsevier |
Issue Date: | 2019 |
ISSN: | 1361-8415 1361-8423 |
Statement of Responsibility: | Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro |
Abstract: | We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). Our proposed post-hoc method automatically diagnosis the whole volume and, for positive cases, it localizes the malignant lesions that led to such diagnosis. Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy - this approach is trained using strongly annotated data (i.e., it needs a delineation and classification of all lesions in an image). We also aim to establish the advantages and disadvantages of both approaches when applied to breast screening from DCE-MRI. Relying on experiments on a breast DCE-MRI dataset that contains scans of 117 patients, our results show that the post-hoc method is more accurate for diagnosing the whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method achieves an AUC of 0.81. However, the performance for localising the malignant lesions remains challenging for the post-hoc method due to the weakly labelled dataset employed during training. |
Keywords: | Magnetic resonance imaging; breast screening; meta-learning; few-shot learning; weakly supervised learning; strongly supervised learning; model interpretation; lesion detection; deep reinforcement learning |
Rights: | © 2019 Elsevier B.V. All rights reserved. |
DOI: | 10.1016/j.media.2019.101562 |
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 |
Published version: | http://dx.doi.org/10.1016/j.media.2019.101562 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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