Lemuel Puglisi

Department of Mathematics

and Computer Science

Office address:

Stanza 200, I blocco

PhD student at University of Catania and Imaging Research Scientist at Queen Square Analytics.

Short bio. I graduated cum laude with a Bachelor’s degree in Computer Science from the University of Catania in July 2021, and I have completed my Master’s degree in Computer Science, specializing in data science, also with a cum laude distinction from the same institution in April 2023. I started my internship at Queen Square Analytics in September 2022 and have joined the QSA team as an imaging research scientist since March 2023. From August 2023, I have started my PhD at University of Catania on data-driven neurodegenerative disease progression modeling under the supervision of Prof. Daniele Ravì.

My research interests center around the application of artificial intelligence (AI) to medical imaging with a focus on the study of neurodegenerative diseases, particularly Multiple Sclerosis and Alzheimer’s disease.

news

Nov 24, 2023	Our work on Automatic Artifact Detection has been published in the Journal of Medical Image Analysis (MEDIA).
Jul 28, 2023	Won the Archimede Award (XIX edition) from University of Catania
Jul 7, 2023	Won a PhD scholarship at University of Catania.
Apr 28, 2023	Received Master’s degree in Computer Science
Jul 27, 2021	Received Bachelor’s degree in Computer Science

selected publications

2023

DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification

Lemuel Puglisi, Arman Eshaghi, Geoff Parker, and 3 more authors

In Medical Imaging with Deep Learning, 2023

Abs PDF Code Poster

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks.