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Some updates
Published:
It’s been a while since the last time I touched this site, but at least I’ve come to update some good news! I’ve added some of the latest publications, DIP-MRF at ISBI, StodIP at the MICCAI workshop MLMI, MRF-IDDPM at ISMRM, and the latest, MRF-DiPh has been accepted at MICCAI!.
Welcome!
Published:
Hello! I have decided to write some posts every now and then with anything that could be worth mentioning. This is not a place exclusively about career content, I will post from time to time about other topics, thoughts and so on! So, in addition to neural networks, MRI and programming, you can expect content about:
publications
Super-Resolution OCT Based on α-Stable Distributions and Sparse Representations
Published in Proceedings of the International BASP Frontiers Workshop 2019, 2019
A collaboration with Daniel Zermeno to explore the potential of the alpha stable distribution for OCT super-resolution.
Recommended citation: Daniel Valdez Zermeno, Perla Mayo, Lindsay Nicholson, and Alin Achim, (2015). "Super-Resolution OCT Based on α-Stable Distributions and Sparse Representations." International Biomedical and Astronomical Signal Processing Frontiers workshop. https://research-information.bris.ac.uk/files/220432394/Valdez18_BASP.pdf
Classification of Alzheimer’s Disease in MRI based on Dictionary Learning and Heavy Tailed Modelling
Published in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
Using alpha Stable distribution with dictionary learning to classify MRI scans.
Recommended citation: Daniel Valdez Zermeno, Perla Mayo, Lindsay Nicholson, and Alin Achim (2019). "Classification of Alzheimer’s Disease in MRI based on Dictionary Learning and Heavy Tailed Modelling." 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://ieeexplore.ieee.org/abstract/document/8857379
Super-Resolution OCT Using Sparse Representations and Heavy-Tailed Models
Published in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
An update on the work published at BASP.
Recommended citation: Daniel Valdez Zermeno, Perla Mayo, Lindsay Nicholson, and Alin Achim. "Super-Resolution OCT Using Sparse Representations and Heavy-Tailed Models." 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 1(2). https://ieeexplore.ieee.org/abstract/document/8857810/
Convergence Guarantees for Non-Convex Optimisation With Cauchy-Based Penalties
Published in IEEE International Conference on Image Processing (ICIP), 2020
The development of the Iterative Cauchy Thresholding algorithm.
Recommended citation: Oktay Karakuş, Perla Mayo, and Alin Achim (2020). "Convergence Guarantees for Non-Convex Optimisation With Cauchy-Based Penalties." 2020 IEEE Transactions in Image Processing. https://ieeexplore.ieee.org/abstract/document/9190736/
Iterative Cauchy Thresholding: Regularisation with a heavy-tailed prior
Published in IEEE International Conference on Image Processing (ICIP), 2020
The development of the Iterative Cauchy Thresholding algorithm.
Recommended citation: Perla Mayo, Robin Holmes, and Alin Achim (2020). "Iterative Cauchy Thresholding: Regularisation with a heavy-tailed prior." 2020 IEEE International Conference on Image Processing (ICIP). https://ieeexplore.ieee.org/abstract/document/9190736/
Representation learning via cauchy convolutional sparse coding
Published in IEEE Access, 2021
A further step in the exploration of the Cauchy distribution in the field of representation learning.
Recommended citation: Perla Mayo, Oktay Karakus, Robin Holmes, and Alin Achim, (2021). "Representation learning via cauchy convolutional sparse coding." IEEE Access. https://ieeexplore.ieee.org/abstract/document/9481269/
Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset
Published in arXiv preprint arXiv:2203.02940, 2022
A framework that exploits two state-of-the-art deep learning models for image enhancement and parasitic egg detection.
Recommended citation: Perla Mayo, Nantheera Anantrasirichai, Thanarat H. Chalidabhongse, Duangdao Palasuwan, and Alin Achim, (2022). "Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset." arXiv. https://arxiv.org/abs/2203.02940
StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations
Published in MLMI 2024, 2022
Efficient implementation of a Deep Image Prior (DIP) approach for the processing of 3D-MRF data.
Recommended citation: Perla Mayo, Matteo Cencini, Ketan Fatania, Carolin M. Pirkl, Marion I. Menzel, Bjoern. H. Menze, Michela Tosetti, and Mohammad Golbabaee, "StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations." in In: Xu, X., Cui, Z., Rekik, I., Ouyang, X., Sun, K. (eds) Machine Learning in Medical Imaging. MLMI 2024. Lecture Notes in Computer Science, vol 15242. Springer, Cham.. https://link.springer.com/chapter/10.1007/978-3-031-73290-4_13
Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders
Published in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024
A method that combines a deep image prior (DIP) module that, without ground truth and in conjunction with a Bloch consistency enforcing autoencoder for MRF reconstruction.
Recommended citation: Perla Mayo, Matteo Cencini, Ketan Fatania, Carolin M. Pirkl, Marion I. Menzel, Bjoern. H. Menze, Michela Tosetti, and Mohammad Golbabaee, "Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders" in 2024 IEEE International Symposium on Biomedical Imaging (ISBI).
Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting
Published in eprint arXiv:2410.23318, 2024
A further step in the exploration of the Cauchy distribution in the field of representation learning.
Recommended citation: Perla Mayo, Carolin M. Pirkl, Alin Achim, Bjoern Menze, and Mohammad Golbabaee. "Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting" in arXiv preprint arXiv:2410.23318, 2024. https://arxiv.org/abs/2410.23318
A probabilistic denoising diffusion-based framework for even higher accelerated quantitative MRI
Published in ISMRM 2025, 2025
Our proposed approach enables the efficient use of Improved Denoising Diffusion Probabilistic Models for reconstructing highly accelerated quantitative MRI acquisitions, such as Magnetic Resonance Fingerprinting, leading to more accurate tissue parameter estimations.
Recommended citation: Perla Mayo, Carolin M. Pirkl, Alin Achim, Bjoern Menze, and Mohammad Golbabaee. "A probabilistic denoising diffusion-based framework for even higher accelerated quantitative MRI" in ISMRM 2025
Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
Published in , 2025
A further step in the exploration of the Cauchy distribution in the field of representation learning.
Recommended citation: Perla Mayo, Carolin M. Pirkl, Alin Achim, Bjoern Menze, and Mohammad Golbabaee. "Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction"