CSASN: A Multitask Attention-Based Framework for Heterogeneous Thyroid Carcinoma Classification in Ultrasound Images
Journal:
arXiv
Published Date:
May 4, 2025
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.
