2 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
Spatial Masked-Set Learning for Sparse Multi-Shell Diffusion MRI Signal Synthesis
Yousef Sadegheih, Pratibha Kumari, Dorit Merhof
Dense multi-shell diffusion MRI provides rich q-space information but requires long acquisition times. We propose a spatial masked-set framework for sparse multi-shell diffusion MR…
Towards Modality-Agnostic Continual Domain-Incremental Brain Lesion Segmentation
Yousef Sadegheih, Dorit Merhof, Pratibha Kumari
Brain lesion segmentation from multi-modal MRI often assumes fixed modality sets or predefined pathologies, making existing models difficult to adapt across cohorts and imaging pro…
Modality-Agnostic Brain Lesion Segmentation with Privacy-aware Continual Learning
Yousef Sadegheih, Pratibha Kumari, Dorit Merhof
Traditional brain lesion segmentation models for multi-modal MRI are typically tailored to specific pathologies, relying on datasets with predefined modalities. Adapting to new MRI…
Continual Domain Incremental Learning for Privacy-aware Digital Pathology
Pratibha Kumari, Daniel Reisenbüchler, Lucas Luttner +3
In recent years, there has been remarkable progress in the field of digital pathology, driven by the ability to model complex tissue patterns using advanced deep-learning algorithm…
LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation
Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari +2
The rise of Transformer architectures has advanced medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers. However,…
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
Pratibha Kumari, Joohi Chauhan, Afshin Bozorgpour +3
Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning al…