MFA-Net: multi-scale feature aggregation network with background-aware module for ultrasound segmentation of thyroid nodules.

ABSTRACT:

Background

The size and morphology of thyroid nodules are the essential basis for distinguishing benign and malignant in clinical diagnosis. However, achieving precise segmentation of these nodules in ultrasound images remains a significant task due to the weak and indistinct edges, low contrast, and complex internal structure. To tackle these challenges, our goal is to develop a multi-scale feature aggregation network (MFA-Net) with background-aware module (BAM), which can effectively and robustly segment ultrasound images of thyroid nodules.

Methods

In MFA-Net framework, through the multi-scale feature aggregation module (MFAM), it effectively captures multi-scale context information and thus improves fine-grained details and global structure representation. Additionally,

SUBMITTER: Ye D 

PROVIDER: S-EPMC12682524 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature