Lesion detection in digital breast tomosynthesis: method, experiences and results of participating to the DBTex challenge

Robert Martí; Pablo G. del Campo; Joel Vidal; Xavier Cufí; Joan Martí; Margarita Chevalier; Jordi Freixenet

doi:10.1117/12.2625733

13 July 2022 Lesion detection in digital breast tomosynthesis: method, experiences and results of participating to the DBTex challenge

Robert Martí, Pablo G. del Campo, Joel Vidal, Xavier Cufí, Joan Martí, Margarita Chevalier, Jordi Freixenet

Author Affiliations +

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860W (2022) https://doi.org/10.1117/12.2625733
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

The paper presents a framework for the detection of mass-like lesions in 3D digital breast tomosynthesis. It consists of several steps, including pre and post-processing, and a main detection block based on a Faster RCNN deep learning network. In addition to the framework, the paper describes different training steps to achieve better performance, including transfer learning using both mammographic and DBT data. The presented approach obtained third place in the recent DBT Lesion detection Challenge, DBTex, being the top approach without using an ensemble based method.

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Robert Martí, Pablo G. del Campo, Joel Vidal, Xavier Cufí, Joan Martí, Margarita Chevalier, and Jordi Freixenet "Lesion detection in digital breast tomosynthesis: method, experiences and results of participating to the DBTex challenge", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860W (13 July 2022); https://doi.org/10.1117/12.2625733

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