Intelligent Diagnosis Using Dual-Branch Attention Network for Rare Thyroid Carcinoma Recognition with Ultrasound Imaging

TL;DR

This paper introduces a dual-branch attention network that combines CNN and Transformer architectures to improve the recognition of rare thyroid carcinomas in ultrasound images, addressing data imbalance and morphological heterogeneity.

Contribution

The study presents a novel multitask learning framework, CSASN, integrating local and global feature extractors with attention modules and a residual classifier for enhanced diagnosis accuracy.

Findings

Outperforms existing models in class-imbalanced conditions

Achieves higher precision and recall for rare subtypes

Demonstrates significant performance gains through ablation studies

Abstract

Heterogeneous morphological features and data imbalance pose significant challenges in rare thyroid carcinoma classification using ultrasound imaging. To address this issue, we propose a novel multitask learning framework, Channel-Spatial Attention Synergy Network (CSASN), which integrates a dual-branch feature extractor - combining EfficientNet for local spatial encoding and ViT for global semantic modeling, with a cascaded channel-spatial attention refinement module. A residual multiscale classifier and dynamically weighted loss function further enhance classification stability and accuracy. Trained on a multicenter dataset comprising more than 2000 patients from four clinical institutions, our framework leverages a residual multiscale classifier and dynamically weighted loss function to enhance classification stability and accuracy. Extensive ablation studies demonstrate that each module contributes significantly to model performance, particularly in recognizing rare subtypes such as FTC and MTC carcinomas. Experimental results show that CSASN outperforms existing single-stream CNN or Transformer-based models, achieving a superior balance between precision and recall under class-imbalanced conditions. This framework provides a promising strategy for AI-assisted thyroid cancer diagnosis.