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Towards improved breast mass detection using dual-view mammogram matching

Abstract : Breast cancer screening benefits from the visual analysis of multiple views of routine mammograms. As for clinical practice, computer-aided diagnosis (CAD) systems could be enhanced by integrating multi-view information. In this work, we propose a new multi-tasking framework that combines craniocaudal (CC) and mediolateral-oblique (MLO) mammograms for automatic breast mass detection. Rather than addressing mass recognition only, we exploit multi-tasking properties of deep networks to jointly learn mass matching and classification, towards better detection performance. Specifically, we propose a unified Siamese network that combines patch-level mass/non-mass classification and dual-view mass matching to take full advantage of multi-view information. This model is exploited in a full image detection pipeline based on You-Only-Look-Once (YOLO) region proposals. We carry out exhaustive experiments to highlight the contribution of dual-view matching for both patch-level classification and examination-level detection scenarios. Results demonstrate that mass matching highly improves the full-pipeline detection performance by outperforming conventional single-task schemes with 94.78% as Area Under the Curve (AUC) score and a classification accuracy of 0.8791. Interestingly, mass classification also improves the performance of mass matching, which proves the complementarity of both tasks. Our method further guides clinicians by providing accurate dual-view mass correspondences, which suggests that it could act as a relevant second opinion for mammogram interpretation and breast cancer diagnosis.
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Contributor : Pierre-Henri Conze <>
Submitted on : Thursday, April 15, 2021 - 12:01:18 PM
Last modification on : Monday, May 3, 2021 - 2:26:31 PM



Yutong Yan, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec, Béatrice Cochener, et al.. Towards improved breast mass detection using dual-view mammogram matching. Medical Image Analysis, Elsevier, 2021, ⟨10.1016/⟩. ⟨hal-03199002⟩



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