Author profile
The University of Chicago
This study investigated performance assessment metrics that, when applied to artificial intelligence (AI) outputs designed to identify the presence of pneumonia on chest radiographs, best align with the subjective clinical opinion of radiologists.
This study aimed to cross validate a conditional generative adversarial network (cGAN) model for noninvasive histological subtype classification of pleural mesothelioma on computed tomography (CT) scans.
Accurate histological subtype classification of pleural mesothelioma (PM) from CT imaging remains challenging due to limited labeled data and substantial morphological variability. Data augmentation is a common strategy used to avoid overfitting when training...
This study addressed class imbalance in automated pleural mesothelioma segmentation and demonstrated that addressing this imbalance significantly improves agreement in the optimal probability map threshold, enabling simultaneous optimization of both spatial o...