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Multi scale curriculum CNN for context-aware breast mri malignancy classification

Haarburger C, Baumgartner M, Truhn D, Broeckmann M, Schneider H, Schrading S, Kuhl C, Merhof D (2019)

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Publication Type: Conference contribution

Publication year: 2019

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 11767 LNCS

Pages Range: 495-503

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030322502

DOI: 10.1007/978-3-030-32251-9_54

Abstract

Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified with respect to malignancy. However, the drawback of this approach is that abstract features incorporating several lesions and areas that are not labelled as a lesion but contain global medically relevant information are thus disregarded: especially for dynamic contrast-enhanced breast MRI, criteria such as background parenchymal enhancement and location within the breast are important for diagnosis and cannot be captured by object detection approaches properly. In this work, we propose a 3D CNN and a multi scale curriculum learning strategy to classify malignancy globally based on an MRI of the whole breast. Thus, the global context of the whole breast rather than individual lesions is taken into account. Our proposed approach does not rely on lesion segmentations, which renders the annotation of training data much more effective than in current object detection approaches. Achieving an AUROC of 0.89, we compare the performance of our approach to Mask R-CNN and Retina U-Net as well as a radiologist. Our performance is on par with approaches that, in contrast to our method, rely on pixelwise segmentations of lesions.

Involved external institutions

How to cite

APA:

Haarburger, C., Baumgartner, M., Truhn, D., Broeckmann, M., Schneider, H., Schrading, S.,... Merhof, D. (2019). Multi scale curriculum CNN for context-aware breast mri malignancy classification. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 495-503). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.

MLA:

Haarburger, Christoph, et al. "Multi scale curriculum CNN for context-aware breast mri malignancy classification." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 495-503.

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