A Machine Learning System to Indicate Diagnosis of Idiopathic Pulmonary Fibrosis Non-Invasively in Challenging Cases

Data from the OSIC Data Repository was used to conduct this research.
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Towards the Adoption of Quantitative Computed Tomography in the Management of Interstitial Lung Disease

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Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

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Enhanced Multimodal Timely Prediction of Pulmonary Fibrosis Progression with Uncertainty Estimation from Chest CT Images and Clinical Metadata

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Lung Parenchyma Segmentation Based on U-Net Fused with Shape Stream

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Beyond Lung Cancer: Leveraging Machine Learning Techniques to Understand Other Diseases

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Application of Neural-Controlled Differential Equations in Disease Progression Modeling Using Irregularly Sampled Multimodal Data

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Vision Intelligence Assisted Lung Function Estimation Based on Transformer Encoder-Decoder Network with Invertible Modeling

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A Bayesian Neural Network-Based Method for the Extraction of a Metabolic Corrected Arterial Input Function From Dynamic [11C]PBR28 PET

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Neural Network Based Method for the Survival Analysis of Idiopathic Pulmonary Fibrosis Patients from a Baseline CT Acquisition

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