Automated classification of fat-infiltrated axillary lymph nodes on screening mammograms.

ABSTRACT:

Objective

Fat-infiltrated axillary lymph nodes (LNs) are unique sites for ectopic fat deposition. Early studies showed a strong correlation between fatty LNs and obesity-related diseases. Confirming this correlation requires large-scale studies, hindered by scarce labeled data. With the long-term goal of developing a rapid and generalizable tool to aid data labeling, we developed an automated deep learning (DL)-based pipeline to classify the status of fatty LNs on screening mammograms.

Methods

Our internal data set included 886 mammograms from a tertiary academic medical institution, with a binary status of the fat-infiltrated LNs based on the size and morphology of the largest visible axillary LN. A two-stage DL model training and fine-tuning pipeline was developed to class

SUBMITTER: Song Q 

PROVIDER: S-EPMC10607412 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature