Multitask Swin Transformer for classification and characterization of pulmonary nodules in CT images.

ABSTRACT:

Background

Early diagnosis of pulmonary nodules is essential for effective prevention and treatment of pulmonary cancer. However, the heterogeneous and complex characteristics of pulmonary nodules, such as shape, size, speculation, and texture, present significant challenges in clinical diagnosis, which computer-aided diagnosis (CAD) can help address. Moreover, the varied performance of deep learning methods in CAD and limited model interpretability often hinder clinicians' understanding of CAD results. In this study, we propose a multitask Swin Transformer (MTST) for classifying benign and malignant pulmonary nodules, which outputs nodule features as classification criteria.

Methods

We introduce a MTST model for feature extraction, designed with a multitask layer that simul

SUBMITTER: Jin H 

PROVIDER: S-EPMC11948416 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature