6 citations · 10 across the 6 of their papers we have counts for
6 papers
Explainability of Complex AI Models with Correlation Impact Ratio
Poushali Sengupta, Rabindra Khadka, Sabita Maharjan +5
Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME…
EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration
Mohammad Mehedi Hasan, Pedro G. Lind, Hernando Ombao +2
Dementia (DEM) is a growing global health challenge, underscoring the need for early and accurate diagnosis. Electroencephalography (EEG) provides a non-invasive window into brain…
From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Saptiotemporal Dynamics in Brain Signal Analysis
Amirabbas Hojjati, Lu Li, Ibrahim Hameed +3
EEG signals capture brain activity with high temporal and low spatial resolution, supporting applications such as neurological diagnosis, cognitive monitoring, and brain-computer i…
DREAMS: A python framework for Training Deep Learning Models on EEG Data with Model Card Reporting for Medical Applications
Rabindra Khadka, Pedro G Lind, Anis Yazidi +1
Electroencephalography (EEG) provides a non-invasive way to observe brain activity in real time. Deep learning has enhanced EEG analysis, enabling meaningful pattern detection for…
Combining datasets to increase the number of samples and improve model fitting
Thu Nguyen, Rabindra Khadka, Nhan Phan +3
For many use cases, combining information from different datasets can be of interest to improve a machine learning model's performance, especially when the number of samples from a…
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
Rabindra Khadga, Debesh Jha, Steven Hicks +4
Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general appli…