This study focuses on gathering a comprehensive dataset of conventional ultrasound images to support the development of advanced training models in medical imaging. With over 3,000 participants, the study aims to create a robust and diverse collection of images representing a wide range of anatomical regions and clinical conditions. The dataset will serve as a foundational resource for improving diagnostic accuracy and enhancing machine learning applications in ultrasound analysis.
View ManualThis annotation process is part of a large-scale effort to label conventional ultrasound images collected from over 3,000 participants (i.e., data collected from Clinical Study A). The primary goal is to generate high-quality, consistent annotations across a wide range of anatomical regions and clinical scenarios. These annotations will serve as critical ground truth data for training and validating machine learning models in medical imaging. Your careful and accurate input ensures the reliability of the dataset and directly contributes to improving diagnostic performance in ultrasound-based AI applications.
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