Mammographic Breast Density Model Using Semi-Supervised Learning Reduces Inter-/Intra-Reader Variability.

Alyssa T Watanabe^1,2, Tara Retson³, Junhao Wang², Richard Mantey², Chiyung Chim², Homa Karimabadi²

¹Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, USA.

²CureMetrix, Inc., San Diego, CA 92101, USA.

³Department of Radiology, University of California, San Diego, CA 92093, USA.

PubMed PMC DOI PDF

Abstract

Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of 928 image pairs and assessed for impact on inter- and intra-reader variability and reading tim…

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