Synthetic data of simulated microcalcification clusters to train and explain deep learning detection models in contrast-enhanced mammography

Astrid Van Camp, Manon Beuque, Lesley Cockmartin, Henry C. Woodruff, Nicholas W. Marshall, Marc Lobbes, Philippe Lambin, Hilde Bosmans

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

31 Downloads (Pure)

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 languageEnglish
Title of host publication16th International Workshop on Breast Imaging
Subtitle of host publicationIWBI 2022
EditorsHilde Bosmans, Nicholas Marshall, Chantal Van Ongeval
PublisherSPIE
Volume12286
ISBN (Print)9781510655843
DOIs
Publication statusPublished - 1 Jan 2022
Event16th International Workshop on Breast Imaging - Leuven, Belgium
Duration: 22 May 202225 May 2022
Conference number: 16

Publication series

SeriesProceedings of SPIE - The International Society for Optical Engineering
Number122860U
Volume12286
ISSN0277-786X

Conference

Conference16th International Workshop on Breast Imaging
Abbreviated titleIWBI 2022
Country/TerritoryBelgium
CityLeuven
Period22/05/2225/05/22

Keywords

  • contrast-enhanced mammography
  • deep learning
  • detection
  • explainability
  • microcalcification clusters
  • simulation framework
  • synthetic data

Fingerprint

Dive into the research topics of 'Synthetic data of simulated microcalcification clusters to train and explain deep learning detection models in contrast-enhanced mammography'. Together they form a unique fingerprint.

Cite this