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Beyond Non-maximum Suppression - Detecting Lesions in Digital Breast Tomosynthesis Volumes

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12905)

Abstract

Detecting the specific locations of malignancy signs in a medical image is a non-trivial and time-consuming task for radiologists. A complex, 3D version of this task, was presented in the DBTex 2021 Grand Challenge on Digital Breast Tomosynthesis Lesion Detection. Teams from all over the world competed in an attempt to build AI models that predict the 3D locations that require biopsy. We describe a novel method to combine detection candidates from multiple models with minimum false positives. This method won the second place in the DBTex competition, with a very small margin from being first and a standout from the rest. We performed an ablation study to show the contribution of each one of the different new components in the proposed ensemble method, including additional performance improvements done after the competition.

Keywords

  • Digital Breast Tomosynthesis
  • Lesion Detection
  • Ensemble
  • DBTex 2021.
  • Deep neural networks

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Correspondence to Vadim Ratner .

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Shoshan, Y., Zlotnick, A., Ratner, V., Khapun, D., Barkan, E., Gilboa-Solomon, F. (2021). Beyond Non-maximum Suppression - Detecting Lesions in Digital Breast Tomosynthesis Volumes. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_74

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  • DOI: https://doi.org/10.1007/978-3-030-87240-3_74

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