Multi-center PET radiomics framework using set-based feature learning and test-time adaptation for patient-level Hodgkin versus non-Hodgkin lymphoma classification.
A multi-center machine learning framework for patient-level classification of Hodgkin and non-Hodgkin lymphoma from baseline FDG-PET imaging. The project represents patients as …
A graph neural network and gaze-guided transformer framework to predict radiological diagnostic errors from eye-tracking data.
Extremal Contours is an algorithmic explainable AI method for extracting compact, smooth, and interpretable visual explanations from deep vision models. The method represents …
SynthRAD2023 challenge report on benchmark evaluation of synthetic CT generation methods for radiotherapy planning.
Attention-based deep learning segmentation for brain tumor delineation.