Hugo Academic CV Theme

Saeed Shurrab

Global PhD Fellow

Saeed Shurrab is a Biomedical Engineering PhD student at the Clinical Artificial Intelligence Lab - New York University Abu Dhabi. He earned his MSc in Data Science and Artificial Intelligence from Jordan University of Science and Technology, where he was awarded the prestigious German Academic Exchange Scholarship (DAAD) and graduated with distinction in 2022. Before this, he completed his BSc in Industrial and Systems Engineering at the Islamic University of Gaza, Palestine, in 2014. Saeed’s passion for data analytics and his strong belief in the transformative power of data in driving robust decision-making and innovative solutions led him to pursue a career in data science.

Featured Publications

CXR

Multimodal masked siamese network improves chest X-ray representation learning

Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. Although such approaches deliver promising results, they do not take advantage of the associated patient or scan information collected within Electronic Health Records (EHR). This study aims to develop a multimodal pretraining approach for chest radiographs that considers EHR data incorporation as an additional modality that during training. We propose to incorporate EHR data during self-supervised pretraining with a Masked Siamese Network (MSN) to enhance the quality of chest radiograph representations. We investigate three types of EHR data, including demographic, scan metadata, and inpatient stay information. We evaluate the multimodal MSN on three publicly available chest X-ray datasets, MIMIC-CXR, CheXpert, and NIH-14, using two vision transformer (ViT) backbones, specifically ViT-Tiny and ViT-Small. In assessing the quality of the representations through linear evaluation, our proposed method demonstrates significant improvement compared to vanilla MSN and state-of-the-art self-supervised learning baselines. In particular, our proposed method achieves an improvement of of 2% in the Area Under the Receiver Operating Characteristic Curve (AUROC) compared to vanilla MSN and 5% to 8% compared to other baselines, including uni-modal ones. Furthermore, our findings reveal that demographic features provide the most significant performance improvement. Our work highlights the potential of EHR-enhanced self-supervised pretraining for medical imaging and opens opportunities for future research to address limitations in existing representation learning methods for other medical imaging modalities, such as neuro-, ophthalmic, and sonar imaging.

Sep 28, 2024

Stroke

Multimodal machine learning for stroke prognosis and diagnosis: A systematic review

Stroke is a life-threatening medical condition that could lead to mortality or significant sensorimotor deficits. Various machine learning techniques have been successfully used to detect and predict stroke-related outcomes. Considering the diversity in the type of clinical modalities involved during management of patients with stroke, such as medical images, bio-signals, and clinical data, multimodal machine learning has become increasingly popular. Thus, we conducted a systematic literature review to understand the current status of state-of-the-art multimodal machine learning methods for stroke prognosis and diagnosis. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during literature search and selection, our results show that the most dominant techniques are related to the fusion paradigm, specifically early, joint and late fusion. We discuss opportunities to leverage other multimodal learning paradigms, such as multimodal translation and alignment, which are generally less explored. We also discuss the scale of datasets and types of modalities used to develop existing models, highlighting opportunities for the creation of more diverse multimodal datasets. Finally, we present ongoing challenges and provide a set of recommendations to drive the next generation of multimodal learning methods for improved prognosis and diagnosis of patients with stroke.

Aug 21, 2024

SSL

Self-supervised learning methods and applications in medical imaging analysis: A survey

The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered an effective solution for the scarcity of annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative, and contrastive approaches. Moreover, the article covers 40 of the most recent research papers in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Finally, the article concludes with possible future research directions in the field.

Jun 28, 2022

Recent Publications

Saeed Shurrab, Alejandro Guerra-Manzanares, Farah E. Shamout (2024). Multimodal masked siamese network improves chest X-ray representation learning. Nature Scientific Reports.

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Saeed Shurrab, Alejandro Guerra-Manzanares, Amani Magid, Bartlomiej Piechowski-Jozwiak, S Farokh Atashzar, Farah E Shamout (2024). Multimodal machine learning for stroke prognosis and diagnosis: A systematic review. IEEE JBHI.

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Saeed Shurrab, Yazan Shannak, Rehab Duwairi (2023). Retina Disorders Classification via OCT Scan: A Comparative Study between Self-Supervised Learning and Transfer Learning. IAJIT.

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Saeed Shurrab, Rehab Duwairi (2022). Self-supervised learning methods and applications in medical imaging analysis: A survey. PeerJ.

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Saeed Shurrab, Robert Ford (2015). An example journal article. Journal of Source Themes, 1(1).

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