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.
View on arXiv@article{sabouri2025_2503.00366,
title={ AI-Augmented Thyroid Scintigraphy for Robust Classification },
author={ Maziar Sabouri and Ghasem Hajianfar and Alireza Rafiei Sardouei and Milad Yazdani and Azin Asadzadeh and Soroush Bagheri and Mohsen Arabi and Seyed Rasoul Zakavi and Emran Askari and Atena Aghaee and Dena Shahriari and Habib Zaidi and Arman Rahmim },
journal={arXiv preprint arXiv:2503.00366},
year={ 2025 }
}