Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method.

ABSTRACT:

Background

In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions.

Methods

A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the cl

SUBMITTER: Meng M 

PROVIDER: S-EPMC10265786 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature