📄 Journal Articles

M. Mehdipour Ghazi, O. Urdanibia-Centelles, A. Bakhtiari, B. Fagerlund, M. B. Vestergaard, H. B. W. Larsson, E. L. Mortensen, M. Osler, M. Nielsen, K. Benedek, and M. Lauritzen
Cognitive aging and reserve factors in the Metropolit 1953 Danish male cohort, GeroScience, 2024. Paper

M. Mehdipour Ghazi, P. Selnes, S. T. Reina, S. Tecelao, S. Ingala, A. Bjørnerud, B. Kirsebom, T. Fladby, and M. Nielsen
Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer’s disease cohorts, Frontiers in Aging Neuroscience, vol. 16, 2024. Paper

N. R. Ferrer, M. V. Sagar, M. Mehdipour Ghazi, K. V. Klein, E. Jimenez-Solem, M. Nielsen, and C. Kruuse
COVID-19 associated cerebral microbleeds in the general population, Brain Communications, vol. 6, p. fcae127, 2024. Paper

A. Bakhtiari, J. Petersen, O. Urdanibia-Centelles, M. Mehdipour Ghazi, B. Fagerlund, E. L. Mortensen, M. Osler, M. Lauritzen, and K. Benedek
Power and distribution of evoked gamma oscillations in brain aging and cognitive performance, GeroScience, 2023. Paper

Zh. Kang, M. Nielsen, B. Yang, and M. Mehdipour Ghazi
Partial feedback online transfer learning with multi-source domains, Information Fusion, vol. 89, pp. 29–40, 2022. Paper

M. Mehdipour Ghazi, L. Sørensen, S. Ourselin, and M. Nielsen
CARRNN: A continuous autoregressive recurrent neural network for deep representation learning from sporadic temporal data, IEEE Transactions on Neural Networks and Learning Systems, vol. 35, pp. 792–802, 2022. Paper • Code

E. Jimenez-Solem et al.
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients, Scientific Reports, vol. 11, pp. 1–12, 2020. Paper

R. V. Marinescu et al.
The Alzheimer’s disease prediction of longitudinal evolution (TADPOLE) challenge: Results after 1-year follow-up, Machine Learning for Biomedical Imaging, pp. 1–10, 2020. Paper

M. Mehdipour Ghazi, M. Nielsen, A. Pai, M. Modat, M. J. Cardoso, S. Ourselin, and L. Sørensen
Robust parametric modeling of Alzheimer’s disease progression, NeuroImage, vol. 225, p. 117460, 2020. Paper • Code

M. Mehdipour Ghazi, M. Nielsen, A. Pai, M. J. Cardoso, M. Modat, S. Ourselin, and L. Sørensen
Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling, Medical Image Analysis, vol. 53, pp. 39–46, 2019. Paper • Code

M. Mehdipour Ghazi, B. Yanikoglu, and E. Aptoula
Plant identification using deep neural networks via optimization of transfer learning parameters, Neurocomputing, vol. 235, pp. 228–235, 2017. Paper

M. Mehdipour Ghazi and H. Erdogan
Image noise level estimation based on higher-order statistics, Multimedia Tools and Applications, vol. 76, pp. 2379–2397, 2017. Paper

📘 Peer-Reviewed Preprints

S. Cerri, V. Nersesjan, K. V. Klein, E. C. Cóppulo, S. N. Llambias, M. Mehdipour Ghazi, M. Nielsen, M. E. Benros
Cross-disorder comparison of brain structures among 4,842 individuals with mental disorders and controls utilizing Danish population-based clinical MRI scans, medRxiv, 2025. Paper

A. Munk, S. Cerri, J. Ambsdorf, J. Machnio, S. N. Llambias, V. Nersesjan, C. H. Krag, P. Liu, P. R. García, M. Mehdipour Ghazi, M. Boesen, M. E. Benros, J. E. Iglesias, M. Nielsen
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning, arXiv preprint arXiv:2506.14432, 2025. Paper

S. N. Llambias, J. Machnio, A. Munk, J. Ambsdorf, M. Nielsen, and M. Mehdipour Ghazi
Yucca: A deep learning framework for medical image analysis, arXiv preprint arXiv:2407.19888, 2024. Paper • Code

M. Mehdipour Ghazi, A. Ramezani, M. Siahi, and M. Mehdipour Ghazi
Learning spatiotemporal features from incomplete data for traffic flow prediction using hybrid deep neural networks, arXiv preprint arXiv:2204.10222, 2022. Paper

📝 Conference & Workshop Papers

A. Moafi, D. Moafi, E. M. Mirkes, G. P. McCann, A. S. Alatrany, J. R. Arnold, M. Mehdipour Ghazi
Robust deep learning for myocardial scar segmentation in cardiac MRI with noisy labels, Proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, Daejeon, South Korea, September 2025. Paper • Code

J. Machnio, S. N. Llambias, M. Nielsen, and M. Mehdipour Ghazi
Towards scalable and robust white matter lesion localization via multimodal deep learning, 2nd Sorbonne-Heidelberg Workshop on AI in Medicine: Machine Learning for Multi-Modal Data, Heidelberg, Germany, June 2025. Paper

S. N. Llambias, M. Nielsen, M. Mehdipour Ghazi
Data augmentation-based unsupervised domain adaptation in medical imaging, SCIA, Reykjavík, Iceland, June 2025. Paper • Code

M. Mehdipour Ghazi and M. Nielsen
FAST-AID Brain: Fast and accurate segmentation tool using artificial intelligence developed for brain, Scandinavian Conference on Image Analysis, Reykjavík, Iceland, June 2025. Paper • Code

J. Machnio, M. Nielsen, and M. Mehdipour Ghazi
Deep learning for localization of white matter lesions in neurological diseases, Proceedings of the 6th Northern Lights Deep Learning Conference, Tromsø, Norway, January 2025. Paper • Code

M. E. Nielsen, M. Nielsen, and M. Mehdipour Ghazi
Assessing the efficacy of classical and deep neuroimaging biomarkers in early Alzheimer’s disease diagnosis, SPIE Medical Imaging Conference, San Francisco, United States, February 2025. Paper • Code

K. V. E. Risager, T. Gholamalizadeh, and M. Mehdipour Ghazi
Non-reference quality assessment for medical imaging: Application to synthetic brain MRIs, MICCAI 2024 Workshop on Deep Generative Models, Marrakesh, Morocco, October 2024. Paper • Code

S. N. Llambias, M. Nielsen, and M. Mehdipour Ghazi
Heterogeneous learning for brain lesion segmentation, detection, and classification, Proceedings of the 5th Northern Lights Deep Learning Conference, PMLR 233, pp. 138-144, Tromsø, Norway, January 2024. Paper

J. Wu, Zh. Kang, S. N. Llambias, M. Mehdipour Ghazi, and M. Nielsen
Active transfer learning for 3D hippocampus segmentation, 2nd International MICCAI Workshop on Medical Image Learning with Limited and Noisy Data, pp. 224–234, Vancouver, Canada, October 2023. Paper

A. Schiavone, S. N. Llambias, J. Johansen, S. Ingala, A. Pai, M. Nielsen, and M. Mehdipour Ghazi
Robust identification of white matter hyperintensities in uncontrolled settings using deep learning, Medical Imaging with Deep Learning, Short Paper Track, Nashville, United States, July 2023. Paper

M. Mehdipour Ghazi and M. Mehdipour Ghazi
GAN-ISI: Generative adversarial networks image source identification using texture analysis, in CLEF2023 Working Notes, CEUR Workshop Proceedings, vol. 3497, September 2023. Paper

N. R. Ferrer, M. V. Sagar, K. V. Klein, C. Kruuse, M. Nielsen, and M. Mehdipour Ghazi
Deep learning-based assessment of cerebral microbleeds in COVID-19, In Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, Cartagena, Colombia, April 2023. Paper

M. Mehdipour Ghazi, M. Nielsen, A. Pai, M. Modat, M. J. Cardoso, S. Ourselin, and L. Sørensen
On the initialization of long short-term memory networks, In Proceedings of the 26th International Conference on Neural Information Processing, Sydney, Australia, December 2019. Paper

M. Mehdipour Ghazi, M. Nielsen, A. Pai, M. J. Cardoso, M. Modat, S. Ourselin, and L. Sørensen
Robust training of recurrent neural networks to handle missing data for disease progression modeling, In the 1st International Conference on Medical Imaging with Deep Learning, Amsterdam, the Netherlands, July 2018. Paper • Code

M. Zimmermann, M. Mehdipour Ghazi, H. K. Ekenel, and J-P Thiran
Combining multiple views for visual speech recognition, In the 14th International Conference on Auditory-Visual Speech Processing, Stockholm, Sweden, August 2017. Paper

M. Zimmermann, M. Mehdipour Ghazi, H. K. Ekenel, and J-P Thiran
Visual speech recognition using PCA networks and LSTMs in a tandem GMM-HMM system, In Asian Conference on Computer Vision, pp. 264–276, Taipei, Taiwan, November 2016. Paper

M. Mehdipour Ghazi, H. K. Ekenel
Automatic emotion recognition in the wild using an ensemble of static and dynamic representations, In Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 514–521, Tokyo, Japan, November 2016. Paper

M. Mehdipour Ghazi, B. Yanikoglu, and E. Aptoula
Open-set plant identification using an ensemble of deep convolutional neural networks, Conference and Labs of the Evaluation Forum, Evora, Portugal, September 2016. Paper

M. Mehdipour Ghazi, H. K. Ekenel
A comprehensive analysis of deep learning based representation for face recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, USA, June 2016. Paper

M. Mehdipour Ghazi, B. Yanikoglu, E. Aptoula, O. Muslu, and M. C. Ozdemir
Sabanci-Okan system in LifeCLEF 2015 plant identification competition, Conference and Labs of the Evaluation Forum, Toulouse, France, September 2015. Paper

A. Shojaei-Hashemi, M. Mehdipour Ghazi, Sh. Ghaemmaghami, H. Soltanian-Zadeh
Universal steganalysis based on local prediction error in wavelet domain, In the 7th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 165–168, Dalian, China, October 2011. Paper

🧠 Clinical Abstracts

K. V. Klein, M. Mehdipour Ghazi, C. Rostrup, B. H. Hestoy, W. L. Pedersen, M. Diamant, and M. Nielsen
Automatic brain MRI segmentation quality control for more reliable study outcomes, RSNA 2023, Chicago, United States, November 2023. Paper

M. Mehdipour Ghazi, L. Sørensen, A. Pai, M. J. Cardoso, M. Modat, S. Ourselin, and M. Nielsen
Disease progression modeling‐based prediction of cognitive decline, Alzheimer’s Association International Conference, Amsterdam, the Netherlands, July 2020. Paper

M. Mehdipour Ghazi, M. Nielsen, A. Pai, M. Modat, M. J. Cardoso, S. Ourselin, and L. Sørensen
MRI biomarkers improve disease progression modeling-based prediction of cognitive decline, RSNA 2019-105th Scientific Assembly and Annual Meeting, Illinois, United States, December 2019. Paper