Farah E. Shamout
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View article: MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning
MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning Open
The aim of multimodal neural networks is to combine diverse data sources, referred to as modalities, to achieve enhanced performance compared to relying on a single modality. However, training of multimodal networks is typically hindered b…
View article: BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity
BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity Open
One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables model training across multiple clients, existing frameworks, …
View article: MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks
MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks Open
Large Language Models (LLMs) have demonstrated significant promise for various applications in healthcare. However, their efficacy in the Arabic medical domain remains unexplored due to the lack of high-quality domain-specific datasets and…
View article: Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data
Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data Open
Stroke is a major public health problem, affecting millions worldwide. Deep learning has recently demonstrated promise for enhancing the diagnosis and risk prediction of stroke. However, existing methods rely on costly medical imaging moda…
View article: Uncertainty Quantification for Machine Learning in Healthcare: A Survey
Uncertainty Quantification for Machine Learning in Healthcare: A Survey Open
Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healthcare, optimizing resources and improving patient care. Despite the emergence of ML-based c…
View article: MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks Open
Multimodal fusion leverages information across modalities to learn better feature representations with the goal of improving performance in fusion-based tasks. However, multimodal datasets, especially in medical settings, are typically sma…
View article: AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering
AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering Open
View article: Assisted Reproductive Technology Dataset of Embryo Time-lapse Images and Clinical Data
Assisted Reproductive Technology Dataset of Embryo Time-lapse Images and Clinical Data Open
In this report, we present Version 1.0 of the Assisted Reproductive Technology (ART) Dataset, a multi-modal fertility dataset from treatments performed at the ART Fertility Clinic in Abu Dhabi, United Arab Emirates, between 2015 and 2022. …
View article: Multimodal masked siamese network improves chest X-ray representation learning
Multimodal masked siamese network improves chest X-ray representation learning Open
View article: The Role of Functional Muscle Networks in Improving Hand Gesture Perception for Human-Machine Interfaces
The Role of Functional Muscle Networks in Improving Hand Gesture Perception for Human-Machine Interfaces Open
Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface el…
View article: Multi-modal Masked Siamese Network Improves Chest X-Ray Representation Learning
Multi-modal Masked Siamese Network Improves Chest X-Ray Representation Learning Open
Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. While such approaches deliver promising results, they do not leverage associated patient or scan information collected within El…
View article: O-246 A cutting-edge artificial intelligence system tracking embryo development and pinpointing the optimal day for blastocyst utilization
O-246 A cutting-edge artificial intelligence system tracking embryo development and pinpointing the optimal day for blastocyst utilization Open
Study question Can an AI model predict at which specific day (D) of development a blastocyst will reach expansion at a usable stage for biopsy/cryopreservation/transfer? Summary answer Our model achieved a high accuracy tracking embryos’ d…
View article: Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification Open
Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of uncertainty have largely been limited to applications of ad…
View article: Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data
Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data Open
Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models analyze each modality independently, missing opportunities t…
View article: Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits
Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits Open
Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper…
View article: Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates
Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates Open
Background Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outc…
View article: Improving Information Extraction from Pathology Reports using Named Entity Recognition
Improving Information Extraction from Pathology Reports using Named Entity Recognition Open
Pathology reports are considered the gold standard in medical research due to their comprehensive and accurate diagnostic information. Natural language processing (NLP) techniques have been developed to automate information extraction from…
View article: Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs
Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs Open
Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medic…
View article: Privacy-preserving machine learning for healthcare: open challenges and future perspectives
Privacy-preserving machine learning for healthcare: open challenges and future perspectives Open
Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be co…
View article: 10 Development and evaluation of a machine learning model to predict positive urine cultures in the outpatient setting and minimize the use of antibiotics
10 Development and evaluation of a machine learning model to predict positive urine cultures in the outpatient setting and minimize the use of antibiotics Open
Objective Excessive prescription of antibiotics is amongst the principal drivers of antibiotic resistance, which is considered a surging threat to global health. The most frequent resistant pathogens are usually linked with urinary tract d…
View article: Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients
Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients Open
Unrecognized patient deterioration can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or …
View article: MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images Open
Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asyn…
View article: An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates
An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates Open
Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree prog…
View article: An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates
An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates Open
Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree prog…
View article: Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic
Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic Open
View article: Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data Open
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in decision-making. However, most of the current state-of-the-art …
View article: Towards dynamic multi-modal phenotyping using chest radiographs and\n physiological data
Towards dynamic multi-modal phenotyping using chest radiographs and\n physiological data Open
The healthcare domain is characterized by heterogeneous data modalities, such\nas imaging and physiological data. In practice, the variety of medical data\nassists clinicians in decision-making. However, most of the current\nstate-of-the-a…
View article: Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams
Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams Open
View article: Meta-repository of screening mammography classifiers
Meta-repository of screening mammography classifiers Open
Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of pr…
View article: Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs Open
Despite the success of deep neural networks in chest X-ray (CXR) diagnosis, supervised learning only allows the prediction of disease classes that were seen during training. At inference, these networks cannot predict an unseen disease cla…