Annotation-free deep-learning framework for microcalcifications detection on mammograms

Breast cancer detection at an early stage significantly increases the chances of recovery for patients. Mammography (MG) is one of the most popular non-invasive and high-resolution imaging allowing radiologists to depict early signs of the disease. Microcalcifications (MCs) often occupy less than 1mm in size and can represent a high risk of suspicion depending on the spatial distribution, morphology, and their evolution over time. Their detection is challenging both the clinicians and computer-aided detection tools. In this work, we propose a novel annotation-free framework designed specifically for the MCs detection and trained in a self-supervised manner thanks to the generation of synthetic MCs. Inspired by the UNet3+ architecture, we reduced its number of parameters to make it applicable in practice and added multi-scale features to enrich fine-grained details with more global context information. Both multi-channel segmentation and multi-class classification tasks are implemented in a multi-scale output approach to catch MC of various sizes. We perform a comparison with several state-of-the-art methods, including different flavors of ResNet-22, ConvNeXt, and UN et3+. An analysis of classification and segmentation performances has been done, using the Gradient-weighted Class Activation Mapping method to make classifiers visually explainable. In this study, we used two public datasets, INBreast and Breast MicroCalcifications Dataset for validation and test purposes. We achieved an AUC score of 0.93 in the characterization of malignant MCs while having a semantic segmentation precision of 0.70. To the best of our knowledge, we are the first study claiming segmentation performances on the BMCD dataset.