Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

Sadanand Singh; Thomas Paul Matthews; Meet Shah; Brent Mombourquette; Trevor Tsue; Aaron Long; Ranya Almohsen; Stefano Pedemonte; Jason Su

doi:10.1117/12.2549923

16 March 2020 Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

Sadanand Singh, Thomas Paul Matthews, Meet Shah, Brent Mombourquette, Trevor Tsue, Aaron Long, Ranya Almohsen, Stefano Pedemonte, Jason Su

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131406 (2020) https://doi.org/10.1117/12.2549923
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Mammography-based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exams or procedures, and low sensitivity. Digital breast tomosynthesis (DBT) improves on conventional mammography by increasing both sensitivity and specificity and is becoming common in clinical settings. However, deep learning (DL) models have been developed mainly on conventional 2D full-field digital mammography (FFDM) or scanned film images. Due to a lack of large annotated DBT datasets, it is difficult to train a model on DBT from scratch. In this work, we present methods to generalize a model trained on FFDM images to DBT images. In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images. In the proposed approach, the differences between the FFDM and DBT domains are reduced via HM and then the base model, which was trained on abundant FFDM images, is fine-tuned. When evaluating on image patches extracted around identified findings, we are able to achieve similar areas under the receiver operating characteristic curve (ROC AUC) of ~ 0:9 for FFDM and ~ 0:85 for MIP images, as compared to a ROC AUC of ~ 0:75 when tested directly on MIP images.

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Sadanand Singh, Thomas Paul Matthews, Meet Shah, Brent Mombourquette, Trevor Tsue, Aaron Long, Ranya Almohsen, Stefano Pedemonte, and Jason Su "Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131406 (16 March 2020); https://doi.org/10.1117/12.2549923

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