Morrell, S;
Wojna, Z;
Khoo, CS;
Ourselin, S;
Iglesias, JE;
(2018)
Large-scale mammography CAD with deformable conv-nets.
In: Stoyanov, D and Taylor, Z and Kainz, B and Maicas, G and Beichel, RR and Martel, A and Maier-Hein, L and Bhatia, K and Vercauteren, T and Oktay, O and Carneiro, G and Bradley, AP and Nascimento, J and Min, H and Brown, MS and Jacobs, C and Lassen-Schmidt, B and Mori, K and Petersen, J and San José Estépar, R and Schmidt-Richberg, A and Veiga, C, (eds.)
Image Analysis for Moving Organ, Breast and Thoracic Images: RAMBO 2018, BIA 2018 and TIA 2018: Proceedings.
(pp. pp. 64-72).
Springer: Cham, Switzerland.
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Abstract
State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules. Among them, region-based fully convolutional networks (R-FCN) and deformable convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for speed and low consumption of memory, which is crucial for processing the high resolutions of to 50 μ m used by radiologists. Deformable convolution and pooling can model a wide range of mammographic findings of different morphology and scales, thanks to their versatility. In this study, we present a neural net architecture based on R-FCN/DCN, that we have adapted from the natural image domain to suit mammograms—particularly their larger image size—without compromising resolution. We trained the network on a large, recently released dataset (Optimam) including 6,500 cancerous mammograms. By combining our modern architecture with such a rich dataset, we achieved an area under the ROC curve of 0.879 for breast-wise detection in the DREAMS challenge (130,000 withheld images), which surpassed all other submissions in the competitive phase.
| Type: | Proceedings paper |
|---|---|
| Title: | Large-scale mammography CAD with deformable conv-nets |
| Event: | RAMBO 2018, BIA 2018 and TIA 2018, held in conjunction with MICCAI 2018, 16-20 September 2018, Granada, Spain |
| ISBN-13: | 9783030009458 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-00946-5_7 |
| Publisher version: | https://doi.org/10.1007/978-3-030-00946-5_7 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10059281 |
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