Scholars@Duke

Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline

Publication ,  Conference
Ren, Y; Lu, J; Liang, Z; Grimm, LJ; Kim, C; Taylor-Cho, M; Yoon, S; Marks, JR; Lo, JY
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2021
Published version (DOI)

In mammography and tomosynthesis, radiologists use the geometric relationship of the four standard screening views to detect breast abnormalities. To date, computer aided detection methods focus on formulations based only on a single view. Recent multi-view methods are either black box approaches using methods such as relation blocks, or perform extensive, case-level feature aggregation requiring large data redundancy. In this study, we propose Retina-Match, an end-to-end trainable pipeline for detection, matching, and refinement that can effectively perform ipsilateral lesion matching in paired screening mammography images. We demonstrate effectiveness on a private, digital mammography data set with 1,016 biopsied lesions and 2,000 negative cases.

Duke Scholars

Sora Christina Yoon
Author Sora Christina Yoon Radiology, Breast Imaging
Lars Johannes L Grimm
Author Lars Johannes L Grimm Radiology, Breast Imaging
Connie Eunjung Kim
Author Connie Eunjung Kim Radiology, Breast Imaging
Jeffrey R. Marks
Author Jeffrey R. Marks Surgery, Surgical Sciences
Joseph Yuan-Chieh Lo
Author Joseph Yuan-Chieh Lo Radiology

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

10.1007/978-3-030-87240-3_33

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2021

Volume

12905 LNCS

Start / End Page

345 / 354

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Ren, Y., Lu, J., Liang, Z., Grimm, L. J., Kim, C., Taylor-Cho, M., … Lo, J. Y. (2021). Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12905 LNCS, pp. 345–354). https://doi.org/10.1007/978-3-030-87240-3_33
Ren, Y., J. Lu, Z. Liang, L. J. Grimm, C. Kim, M. Taylor-Cho, S. Yoon, J. R. Marks, and J. Y. Lo. “Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12905 LNCS:345–54, 2021. https://doi.org/10.1007/978-3-030-87240-3_33.
Ren Y, Lu J, Liang Z, Grimm LJ, Kim C, Taylor-Cho M, et al. Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 345–54.
Ren, Y., et al. “Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12905 LNCS, 2021, pp. 345–54. Scopus, doi:10.1007/978-3-030-87240-3_33.
Ren Y, Lu J, Liang Z, Grimm LJ, Kim C, Taylor-Cho M, Yoon S, Marks JR, Lo JY. Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 345–354.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

10.1007/978-3-030-87240-3_33

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2021

Volume

12905 LNCS

Start / End Page

345 / 354

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences