A multitask approach for automated detection and segmentation of thyroid nodules in ultrasound images.

ABSTRACT: An increase in the incidence and diagnosis of thyroid nodules and thyroid cancer underscores the need for a better approach to nodule detection and risk stratification in ultrasound (US) images that can reduce healthcare costs, patient discomfort, and unnecessary invasive procedures. However, variability in ultrasound technique and interpretation makes the diagnostic process partially subjective. Therefore, an automated approach that detects and segments nodules could improve performance on downstream tasks, such as risk stratification. Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9888 images, and annotated by collaborating radiologists. Current deep learning architectures for segmentation are typically semi-automated because they are evaluated solely on imag

SUBMITTER: Radhachandran A 

PROVIDER: S-EPMC11354840 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature