Thyroid Cancer

Thyroid cancer diagnosis is undergoing a transformation driven by artificial intelligence and advanced imaging analysis. Current research heavily focuses on developing and validating deep learning models, including convolutional neural networks and transformers, to improve the accuracy and efficiency of diagnosis using ultrasound images, scintigraphy, and pathology reports. These models are often combined with multimodal data integration and techniques like multiple instance learning to address challenges such as indeterminate nodules and inconsistent labeling, ultimately aiming to reduce overtreatment and improve patient outcomes. This work holds significant promise for streamlining diagnostic workflows and enhancing the precision of thyroid cancer detection and classification.