Thyroid Ultrasound

Thyroid ultrasound image analysis aims to improve the accuracy and efficiency of thyroid nodule diagnosis, particularly in differentiating benign from malignant nodules. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks and transformers, often incorporating multi-view analysis (combining transverse and longitudinal images) and self-supervised learning techniques to overcome data limitations and improve model generalization across different ultrasound machines. These advancements hold significant promise for assisting radiologists in improving diagnostic accuracy and potentially reducing the need for unnecessary biopsies, ultimately leading to better patient care.