110 citations · 165 across the 15 of their papers we have counts for
18 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…
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…
Out-of-distribution Object Detection through Bayesian Uncertainty Estimation
Tianhao Zhang, Shenglin Wang, Nidhal Bouaynaya +2
The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many prac…
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…
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
Ian E. Nielsen, Ravi P. Ramachandran, Nidhal Bouaynaya +2
The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning mode…
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…
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…
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…
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Ian E. Nielsen, Dimah Dera, Ghulam Rasool +2
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decis…
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…
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…