1 code implementation • 4 Mar 2024 • Iryna Hartsock, Ghulam Rasool
Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on models designed for medical report generation and visual question answering (VQA).
no code implementations • 28 Nov 2023 • Jacob R. Epifano, Stephen Glass, Ravi P. Ramachandran, Sharad Patel, Aaron J. Masino, Ghulam Rasool
This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and beyond.
1 code implementation • 30 Sep 2023 • Aakash Tripathi, Asim Waqas, Kavya Venkatesan, Yasin Yilmaz, Ghulam Rasool
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data.
no code implementations • 22 Aug 2023 • Christopher Angelini, Nidhal Bouaynaya, Ghulam Rasool
Catastrophic forgetting is mitigated by using the closed-form ELBO to approximate the Minimum Description Length (MDL) Principle, inherently penalizing changes in the model likelihood by minimizing the KL Divergence between the variational posterior for the current task and the previous task's variational posterior acting as the prior.
no code implementations • 29 Jun 2023 • Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool, Tyler Travis, Lacey Thompson, Charles C. Johnson
Our recent work demonstrated that AI algorithms could use video data from onboard cameras and correctly identify different flight parameters from cockpit gauges, e. g., indicated airspeed.
no code implementations • 22 Jun 2023 • Ian E. Nielsen, Erik Grundeland, Joseph Snedeker, Ghulam Rasool, Ravi P. Ramachandran
Feature visualization is used to visualize learned features for black box machine learning models.
1 code implementation • 22 Mar 2023 • Jacob R. Epifano, Ravi P. Ramachandran, Aaron J. Masino, Ghulam Rasool
In the last few years, many works have tried to explain the predictions of deep learning models.
no code implementations • 15 Mar 2023 • Ian E. Nielsen, Ravi P. Ramachandran, Nidhal Bouaynaya, Hassan M. Fathallah-Shaykh, Ghulam Rasool
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 model.
no code implementations • 11 Mar 2023 • Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran, Paul Stewart, Ghulam Rasool
Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning.
no code implementations • 1 Feb 2023 • Yassine Barhoumi, Nidhal C. Bouaynaya, Ghulam Rasool
Hybrid CNNs and ViTs might provide the desired feature richness for developing accurate medical computer vision models
no code implementations • 28 Apr 2022 • Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ghulam Rasool, Ravi P. Ramachandran
Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision.
no code implementations • 10 Nov 2021 • Giuseppina Carannante, Dimah Dera, Nidhal C. Bouaynaya, Hassan M. Fathallah-Shaykh, Ghulam Rasool
Moreover, the uncertainty map of the proposed SUPER-Net associates low confidence (or equivalently high uncertainty) to patches in the test input images that are corrupted with noise, artifacts, or adversarial attacks.
no code implementations • 10 Nov 2021 • Giuseppina Carannante, Dimah Dera, Ghulam Rasool, Nidhal C. Bouaynaya, Lyudmila Mihaylova
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs).
no code implementations • 10 Nov 2021 • Giuseppina Carannante, Dimah Dera, Ghulam Rasool, Nidhal C. Bouaynaya
We show that Bayesian neural networks automatically discover redundancy in model parameters, thus enabling self-compression, which is linked to the propagation of uncertainty through the layers of the network.
no code implementations • 15 Aug 2021 • Daniel E. Cahall, Ghulam Rasool, Nidhal C. Bouaynaya, Hassan M. Fathallah-Shaykh
Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance.
no code implementations • 23 Jul 2021 • Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Ravi P. Ramachandran
Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations.
1 code implementation • 30 Jun 2021 • Asim Waqas, Ghulam Rasool, Hamza Farooq, Nidhal C. Bouaynaya
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.
no code implementations • 10 Jul 2018 • 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.