Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans.
Wenyu XingYanping YangYanan ZhouTao JiangYifang LiYuanlin SongDongni HouDean TaPublished in: IEEE journal of translational engineering in health and medicine (2024)
The proposed easily-trained and high-performance deep learning model provides a fast and effective way for the diagnosis and dynamic monitoring of pulmonary cavity lesions in clinic. Clinical and Translational Impact Statement: This model used artificial intelligence to achieve the detection and quantitative analysis of pulmonary cavity lesions in CT scans. The morphological features revealed in experiments can be utilized as potential indicators for diagnosis and dynamic monitoring of patients with cavity lesions.
Keyphrases
- deep learning
- artificial intelligence
- computed tomography
- machine learning
- pulmonary hypertension
- dual energy
- contrast enhanced
- big data
- convolutional neural network
- image quality
- high resolution
- positron emission tomography
- primary care
- magnetic resonance imaging
- single cell
- resistance training
- quantum dots
- mass spectrometry