AI-Augmented Thyroid Scintigraphy for Robust Classification
Journal:
arXiv
Published Date:
Mar 1, 2025
Abstract
Thyroid scintigraphy is a key imaging modality for diagnosing thyroid
disorders. Deep learning models for thyroid scintigraphy classification often
face challenges due to limited and imbalanced datasets, leading to suboptimal
generalization. In this study, we investigate the effectiveness of different
data augmentation techniques including Stable Diffusion (SD), Flow Matching
(FM), and Conventional Augmentation (CA) to enhance the performance of a
ResNet18 classifier for thyroid condition classification. Our results showed
that FM-based augmentation consistently outperforms SD-based approaches,
particularly when combined with original (O) data and CA (O+FM+CA), achieving
both high accuracy and fair classification across Diffuse Goiter (DG), Nodular
Goiter (NG), Normal (NL), and Thyroiditis (TI) cases. The Wilcoxon statistical
analysis further validated the superiority of O+FM and its variants (O+FM+CA)
over SD-based augmentations in most scenarios. These findings highlight the
potential of FM-based augmentation as a superior approach for generating
high-quality synthetic thyroid scintigraphy images and improving model
generalization in medical image classification.
