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VLST: Virtual Lung Screening Trial for Lung Cancer Detection Using Virtual Imaging Trial
Objectives: To establish a virtual imaging trial (VIT) platform that accurately simulates real-world lung screening trials (LSTs) to assess the diagnostic accuracy of CT and CXR modalities.
A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth
We evaluated our method on multiple clinically-relevant endpoints, including time to intracranial progression (ICP), progression-free survival (PFS) after SRS, overall survival (OS), and time to ICP and/or death (ICPD), on a variety of both statistical and non-statistical models, including CoxPH, conditional survival forest (CSF), and neural multi-task linear regression (NMTLR).
Duke Spleen Data Set: A Publicly Available Spleen MRI and CT dataset for Training Segmentation
Spleen volumetry is primarily associated with patients suffering from chronic liver disease and portal hypertension, as they often have spleens with abnormal shapes and sizes.
A Radiomics-Boosted Deep-Learning Model for COVID-19 and Non-COVID-19 Pneumonia Classification Using Chest X-ray Image
To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-learning models were trained based on a pre-trained VGG-16 architecture: in the 1st model, X-ray image was the sole input; in the 2nd model, X-ray image and 2 radiomic feature maps (RFM) selected by the saliency map analysis of the 1st model were stacked as the input.
