11 citations · 14 across the 5 of their papers we have counts for
5 papers
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…
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation
Daniel E. Cahall, Ghulam Rasool, Nidhal C. Bouaynaya +1
Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance. Recently, various models based on deep neural net…
Two-Dimensional ARMA Modeling for Breast Cancer Detection and Classification
Nidhal Bouaynaya, Jerzy Zielinski, Dan Schonfeld
We propose a new model-based computer-aided diagnosis (CAD) system for tumor detection and classification (cancerous v.s. benign) in breast images. Specifically, we show that (x-ra…