Ultrasound Dataset

Ultrasound datasets are crucial for developing and validating machine learning models for medical image analysis, addressing the challenges of limited and costly annotated data. Current research focuses on creating and utilizing large, publicly available datasets for tasks like anatomical segmentation and disease detection, employing various deep learning architectures including convolutional neural networks (CNNs), transformers, and prompt-based models like Segment Anything Model (SAM) adaptations. These efforts aim to improve diagnostic accuracy, efficiency, and accessibility of ultrasound-based healthcare, particularly in areas like cardiac, musculoskeletal, and breast imaging, while also addressing issues of model fairness and variability in expert annotations.