construction-validation-nomogram-to-identify-mucus-obstruction

Construction and Validation of A Nomogram to Identify Mucus Obstruction In Patients With Chronic Obstructive Pulmonary Disease

Small airway mucus impaction in chest computed tomography (CT) is a clinically significant finding in chronic obstructive pulmonary disease (COPD), ...

The First Affiliated Hospital of Shenzhen University (Shenzhen Second People's Hospital)

AI-driven tumor recognition unlocks new possibilities for precise tumor screening and diagnosis. However, the progress is heavily hampered by the scarcity of annotated datasets, demanding extensive efforts by radiologists. To this end, we introduce FreeTumor, a Generative AI framework to enable large-scale tumor synthesis for mitigating data scarcity. Specifically, FreeTumor effectively leverages limited labeled data and large-scale unlabeled data for training. Unleashing the power of large-scale data, FreeTumor is capable of synthesizing a large number of realistic tumors for augmenting training datasets. We curate a large-scale dataset comprising 161,310 Computed Tomography (CT) volumes for tumor synthesis and recognition, with only 2.3% containing annotated tumors. 13 board-certified radiologists are engaged to discern between synthetic and real tumors, rigorously validating the quality of synthetic tumors. Through high-quality tumor synthesis, FreeTumor showcases a notable superiority over state-of-the-art tumor recognition methods, indicating promising prospects in clinical applications.

Yanning Zhou

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