4 papers
SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networks
Giuseppina Carannante, Nidhal C. Bouaynaya, Dimah Dera +2
Deep Learning (DL) holds great promise in reshaping the industry owing to its precision, efficiency, and objectivity. However, the brittleness of DL models to noisy and out-of-dist…
DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction
Kyle Naddeo, Nikolas Koutsoubis, Rahul Krish +4
Access to medical imaging and associated text data has the potential to drive major advances in healthcare research and patient outcomes. However, the presence of Protected Health…
Medical Image De-Identification Benchmark Challenge
Linmin Pei, Granger Sutton, Michael Rutherford +67
The de-identification (deID) of protected health information (PHI) and personally identifiable information (PII) is a fundamental requirement for sharing medical images, particular…
Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation
Christopher Angelini, Nidhal Bouaynaya
When fine-tuning Deep Neural Networks (DNNs) to new data, DNNs are prone to overwriting network parameters required for task-specific functionality on previously learned tasks, res…