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Yan Y, Conze PH, Lamard M, Quellec G, Cochener B, Coatrieux G. Towards improved breast mass detection using dual-view mammogram matching. Med Image Anal 2021; 71:102083. [PMID: 33979759 DOI: 10.1016/j.media.2021.102083] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 02/18/2021] [Accepted: 04/14/2021] [Indexed: 11/18/2022]
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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Affiliation(s)
- Yutong Yan
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France
| | - Pierre-Henri Conze
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France.
| | - Mathieu Lamard
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France
| | - Gwenolé Quellec
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France
| | - Béatrice Cochener
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France; CHRU de Brest, 2 avenue Foch, Brest 29200, France
| | - Gouenou Coatrieux
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France
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