Combining curriculum learning and weakly supervised attention for enhanced thyroid nodule assessment in ultrasound imaging.

ABSTRACT:

Background

The accurate assessment of thyroid nodules, which are increasingly common with age and lifestyle factors, is essential for early malignancy detection. Ultrasound imaging, the primary diagnostic tool for this purpose, holds promise when paired with deep learning. However, challenges persist with small datasets, where conventional data augmentation can introduce noise and obscure essential diagnostic features. To address dataset imbalance and enhance model generalization, this study integrates curriculum learning with a weakly supervised attention network to improve diagnostic accuracy for thyroid nodule classification.

Methods

This study integrates curriculum learning with attention-guided data augmentation to improve deep learning model performance in classifying

SUBMITTER: Keatmanee C 

PROVIDER: S-EPMC12397710 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature