2 citations · 4 across the 5 of their papers we have counts for
5 papers
FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation
Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot
Multi-organ segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and diverse annotated datasets. P…
A Few Cases Are All You Need: An Empirical Study of Annotation-Efficient LoRA Fine-Tuning of MedSAM3
Sachin Dudda Nagaraju, Bendik Skarre Abrahamsen, Ashkan Moradi +1
Medical image segmentation is essential for clinical workflows such as treatment planning and disease assessment. While specialist tools like TotalSegmentator and MRSegmentator ach…
Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization
Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen +1
Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and differ in modality and acquisition p…
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…
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 (…