2 citations · 3 across the 8 of their papers we have counts for
13 papers
QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Models
Mostafa Mehdipour Ghazi
Adapting large pretrained vision models under limited data and frozen-backbone constraints remains a central challenge in transfer learning. While lightweight adapters and paramete…
CATCH: Counterfactual Anatomical Tissue Inpainting with Conditional Haar Diffusion
Simon Winther Albertsen, Hjalte Bjoernstrup, Said Djafar Said +1
BraTS local synthesis replaces masked regions in T1-weighted brain MRI with plausible tumor-free tissue while preserving observed anatomy. We present CATCH, conditional 3D diffusio…
ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning
Noah Videcrantz, Mostafa Mehdipour Ghazi
Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, either relying on expensive dataset-…
Do 3D Medical Foundation Models See Through MRI Artifacts? A Controlled Study of Representation Robustness
Julia Anna Mielcarz, Daniel Klaaby, Mostafa Mehdipour Ghazi
Self-supervised 3D medical foundation models are increasingly used as general-purpose feature extractors, yet their sensitivity to MRI artifacts remains poorly understood. We prese…
How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification
Julia Machnio, Mads Nielsen, Mostafa Mehdipour Ghazi
Active learning (AL) promises to reduce the cost of medical imaging projects by lowering the number of clinical labels required. However, practical deployment requires committing t…
A Mechanism-Driven Theory of Phase Transitions in Active Learning
Julia Machnio, Mads Nielsen, Mostafa Mehdipour Ghazi
Active learning (AL) performance is known to be budget-dependent, yet regimes are typically defined by heuristic label counts that fail to generalize across datasets or architectur…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
To Label or Not to Label: PALM -- A Predictive Model for Evaluating Sample Efficiency in Active Learning Models
Julia Machnio, Mads Nielsen, Mostafa Mehdipour Ghazi
Active learning (AL) seeks to reduce annotation costs by selecting the most informative samples for labeling, making it particularly valuable in resource-constrained settings. Howe…
Yucca: A Deep Learning Framework For Medical Image Analysis
Sebastian Nørgaard Llambias, Julia Machnio, Asbjørn Munk +3
Medical image analysis using deep learning frameworks has advanced healthcare by automating complex tasks, but many existing frameworks lack flexibility, modularity, and user-frien…
Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19
Neus Rodeja Ferrer, Malini Vendela Sagar, Kiril Vadimovic Klein +3
Cerebral Microbleeds (CMBs), typically captured as hypointensities from susceptibility-weighted imaging (SWI), are particularly important for the study of dementia, cerebrovascular…
Learning spatiotemporal features from incomplete data for traffic flow prediction using hybrid deep neural networks
Mehdi Mehdipour Ghazi, Amin Ramezani, Mehdi Siahi +1
Urban traffic flow prediction using data-driven models can play an important role in route planning and preventing congestion on highways. These methods utilize data collected from…
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data
Mostafa Mehdipour Ghazi, Lauge Sørensen, Sébastien Ourselin +1
Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the d…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…