Abstract
Deep learning (DL) models can be trained on contrast-enhanced mammography (CEM) images to detect and classify lesions in the breast. As they often put more emphasis on the masses enhanced in the recombined image, they can fail in recognizing microcalcification clusters since these are hardly enhanced and are mainly visible in the (processed) lowenergy image. Therefore, we developed a method to create synthetic data with simulated microcalcification clusters to be used for data augmentation and explainability studies when training DL models. At first 3-dimensional voxel models of simulated microcalcification clusters based on descriptors of the shape and structure were constructed. In a set of 500 simulated microcalcification clusters the range of the size and of the number of microcalcifications per cluster followed the distribution of real clusters. The insertion of these clusters in real images of non-delineated CEM cases was evaluated by radiologists. The realism score was acceptable for single view applications. Radiologists could more easily categorize synthetic clusters into benign versus malignant than real clusters. In a second phase of the work, the role of synthetic data for training and/or explaining DL models was explored. A Mask R-CNN model was trained with synthetic CEM images containing microcalcification clusters. After a training run of 100 epochs the model was found to overfit on a training set of 192 images. In an evaluation with multiple test sets, it was found that this high level of sensitivity was due to the model being capable of recognizing the image rather than the cluster. Synthetic data could be applied for more tests, such as the impact of particular features in both background and lesion models.
Original language | English |
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Title of host publication | 16th International Workshop on Breast Imaging |
Subtitle of host publication | IWBI 2022 |
Editors | Hilde Bosmans, Nicholas Marshall, Chantal Van Ongeval |
Publisher | SPIE |
Volume | 12286 |
ISBN (Print) | 9781510655843 |
DOIs | |
Publication status | Published - 1 Jan 2022 |
Event | 16th International Workshop on Breast Imaging - Leuven, Belgium Duration: 22 May 2022 → 25 May 2022 Conference number: 16 |
Publication series
Series | Proceedings of SPIE - The International Society for Optical Engineering |
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Number | 122860U |
Volume | 12286 |
ISSN | 0277-786X |
Conference
Conference | 16th International Workshop on Breast Imaging |
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Abbreviated title | IWBI 2022 |
Country/Territory | Belgium |
City | Leuven |
Period | 22/05/22 → 25/05/22 |
Keywords
- contrast-enhanced mammography
- deep learning
- detection
- explainability
- microcalcification clusters
- simulation framework
- synthetic data