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20222025
most citedExplaining Human Activity Recognition with SHAP: Validating Insights with Perturbation and Quantitative Measures

27 citations

8 papers

cs.CV20252 cited

FedGIN: Federated Learning with Dynamic Global Intensity Non-linear Augmentation for Organ Segmentation using Multi-modal Images

Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen +1

Medical image segmentation plays a crucial role in AI-assisted diagnostics, surgical planning, and treatment monitoring. Accurate and robust segmentation models are essential for e…

eess.IV20252 cited

Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study

Ashkan Moradi, Fadila Zerka, Joeran S. Bosma +7

Purpose: To develop and optimize a federated learning (FL) framework across multiple clients for biparametric MRI prostate segmentation and clinically significant prostate cancer (…

cond-mat.dis-nn202510 cited

The unbearable lightness of Restricted Boltzmann Machines: Theoretical Insights and Biological Applications

Giovanni di Sarra, Barbara Bravi, Yasser Roudi

Restricted Boltzmann Machines are simple yet powerful neural networks. They can be used for learning structure in data, and are used as a building block of more complex neural arch…

cs.LG202410 cited

Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition

Felix Tempel, Daniel Groos, Espen Alexander F. Ihlen +2

Explaining machine learning (ML) models using eXplainable AI (XAI) techniques has become essential to make them more transparent and trustworthy. This is especially important in hi…

physics.med-ph2024

A novel imaging setup for hybrid radiotherapy tailored PET/MR in patients with head and neck cancer

R. M. Winter, O. Engelsen, O. J. Bratting +4

Purpose: Radiotherapy commonly relies on CT, but there is growing interest in using hybrid PET/MR. Therefore, dedicated hardware setups have been proposed for PET/MR systems which…

cs.CV202427 cited

Explaining Human Activity Recognition with SHAP: Validating Insights with Perturbation and Quantitative Measures

Felix Tempel, Espen Alexander F. Ihlen, Lars Adde +1

In Human Activity Recognition (HAR), understanding the intricacy of body movements within high-risk applications is essential. This study uses SHapley Additive exPlanations (SHAP)…