2 citations · 5 across the 11 of their papers we have counts for
11 papers
Fast and Accurate Monomodal 3D High Resolution Deep Registration of Drosophila Larval Brain Volumes
Daniel Reisenbüchler, Yousef Sadegheih, Michael Dittrich +3
The larval stage of Drosophila melanogaster is a compact model system for neuroscience whose genetic toolkit allows fluorescent markers to be expressed in defined neural population…
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…
Footprint-Guided Exemplar-Free Continual Histopathology Report Generation
Pratibha Kumari, Daniel Reisenbüchler, Afshin Bozorgpour +3
Rapid progress in vision-language modeling has enabled pathology report generation from gigapixel whole-slide images, but most approaches assume static training with simultaneous a…
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…
Attention-based Generative Latent Replay: A Continual Learning Approach for WSI Analysis
Pratibha Kumari, Daniel Reisenbüchler, Afshin Bozorgpour +3
Whole slide image (WSI) classification has emerged as a powerful tool in computational pathology, but remains constrained by domain shifts, e.g., due to different organs, diseases,…
Domain-incremental White Blood Cell Classification with Privacy-aware Continual Learning
Pratibha Kumari, Afshin Bozorgpour, Daniel Reisenbüchler +4
White blood cell (WBC) classification plays a vital role in hematology for diagnosing various medical conditions. However, it faces significant challenges due to domain shifts caus…
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…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
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…