26 citations · 57 across the 14 of their papers we have counts for
14 papers
Leveraging Agonistic-Antagonistic Coactivation in Single-Grid HDsEMG for Hand Gesture Recognition
Firas Darwish, Dhiyaa Al Jorf, Costanza Armanini +2
Surface Electromyography (sEMG) is critical for intention prediction in human-computer interfaces, such as for prosthetics control. Although deep learning models for Hand Gesture R…
AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks
Baraa Al Jorf, Farah E. Shamout
Building effective clinical decision support systems requires the synthesis of complex heterogeneous multimodal data. Such modalities include temporal electronic health records dat…
MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning
Alejandro Guerra-Manzanares, Farah E. Shamout
The aim of multimodal neural networks is to combine diverse data sources, referred to as modalities, to achieve enhanced performance compared to relying on a single modality. Howev…
BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity
Alejandro Guerra-Manzanares, Omar El-Herraoui, Michail Maniatakos +1
One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables mo…
MedPatch: Confidence-Guided Multi-Stage Fusion for Multimodal Clinical Data
Baraa Al Jorf, Farah Shamout
Clinical decision-making relies on the integration of information across various data modalities, such as clinical time-series, medical images and textual reports. Compared to othe…
Uncertainty Quantification for Machine Learning in Healthcare: A Survey
L. Julián Lechuga López, Shaza Elsharief, Dhiyaa Al Jorf +3
Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healthcare, optimizing resources and…