
The provided text introduces CACTUS, a novel open dataset of graded cardiac ultrasound images intended to advance automated analysis in cardiology. The authors present a deep learning framework leveraging transfer learning for both classifying cardiac views and assessing image quality. This framework, trained on the CACTUS dataset, aims to assist medical professionals by automating the time-consuming and error-prone tasks of ultrasound image evaluation, achieving high accuracy in classification and low error in grading. The research addresses the limited availability of public cardiac ultrasound data and the lack of graded datasets for quality assessment, offering a valuable resource and a promising approach for real-time cardiac ultrasound analysis.
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