17 citations · 59 across the 10 of their papers we have counts for
13 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…
Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual Learning
Hikmat Khan, Ghulam Rasool, Nidhal Carla Bouaynaya
Continual learning focuses on learning non-stationary data distribution without forgetting previous knowledge. Rehearsal-based approaches are commonly used to combat catastrophic f…
Variational Density Propagation Continual Learning
Christopher Angelini, Nidhal Bouaynaya, Ghulam Rasool
Deep Neural Networks (DNNs) deployed to the real world are regularly subject to out-of-distribution (OoD) data, various types of noise, and shifting conceptual objectives. This pap…
Deep Ensemble for Rotorcraft Attitude Prediction
Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool +3
Historically, the rotorcraft community has experienced a higher fatal accident rate than other aviation segments, including commercial and general aviation. Recent advancements in…
The Importance of Robust Features in Mitigating Catastrophic Forgetting
Hikmat Khan, Nidhal C. Bouaynaya, Ghulam Rasool
Continual learning (CL) is an approach to address catastrophic forgetting, which refers to forgetting previously learned knowledge by neural networks when trained on new tasks or d…
Efficient Scopeformer: Towards Scalable and Rich Feature Extraction for Intracranial Hemorrhage Detection
Yassine Barhoumi, Nidhal C. Bouaynaya, Ghulam Rasool
The quality and richness of feature maps extracted by convolution neural networks (CNNs) and vision Transformers (ViTs) directly relate to the robust model performance. In medical…
Robust Learning via Ensemble Density Propagation in Deep Neural Networks
Giuseppina Carannante, Dimah Dera, Ghulam Rasool +2
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for…
Self-Compression in Bayesian Neural Networks
Giuseppina Carannante, Dimah Dera, Ghulam Rasool +1
Machine learning models have achieved human-level performance on various tasks. This success comes at a high cost of computation and storage overhead, which makes machine learning…
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…
Scopeformer: n-CNN-ViT Hybrid Model for Intracranial Hemorrhage Classification
Yassine Barhoumi, Ghulam Rasool
We propose a feature generator backbone composed of an ensemble of convolutional neuralnetworks (CNNs) to improve the recently emerging Vision Transformer (ViT) models. We tackled…
Exploring Robust Architectures for Deep Artificial Neural Networks
Asim Waqas, Ghulam Rasool, Hamza Farooq +1
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance. However, the relationship between the architecture of a…
Constrained State Estimation -- A Review
Nesrine Amor, Ghulam Rasool, Nidhal C. Bouaynaya
The real-world applications in signal processing generally involve estimating the system state or parameters in nonlinear, non-Gaussian dynamic systems. The estimation problem may…