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Abstract Pulmonary tumor diagnosis with ultrasound is clinically valuable yet technically challenging. Although deep learning has shown promise in supporting sonographers, progress is hindered by the lack of publicly available, high-quality annotated datasets. Given the limited clinical adoption of pulmonary ultrasound for differentiating benign and malignant tumors, publicly available datasets for AI research in this area remain scarce.
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