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|Scopus||Web of Science®||Altmetric|
|Title:||Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI|
|Citation:||Medical Image Analysis, 2019; 58:101562-1-101562-14|
|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.|
|Appears in Collections:||Computer Science publications|
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