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.
Author profile
Hedy L. Kindler, MD
The University of Chicago
Cross Validation of a Conditional Generative Adversarial Network for the Classification of Pleural Mesothelioma Histological Subtype on CT Scans
Poster Program · Diagnostic and Interventional Radiology Physics
Effect of Data Augmentation on Classification Performance of Pleural Mesothelioma Histological Subtype on 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...
Poster Program · Diagnostic and Interventional Radiology Physics
Investigation of Weighted Binary Cross Entropy and Resnet-50 on Segmentation Performance of Pleural Mesothelioma on CT Scans
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...
Poster Program · Diagnostic and Interventional Radiology Physics