1 citations · 1 across the 7 of their papers we have counts for
7 papers
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images
Mohammad Areeb Qazi, Ibrahim Almakky, Anees Ur Rehman Hashmi +2
Continual learning, the ability to acquire knowledge from new data while retaining previously learned information, is a fundamental challenge in machine learning. Various approache…
TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation
Santosh Sanjeev, Fadillah Adamsyah Maani, Arsen Abzhanov +4
With the emergence of vision language models in the medical imaging domain, numerous studies have focused on two dominant research activities: (1) report generation from Chest X-ra…
Multi-Task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans
Mohammad Areeb Qazi, Mohammed Talha Alam, Ibrahim Almakky +3
Precise estimation of fetal biometry parameters from ultrasound images is vital for evaluating fetal growth, monitoring health, and identifying potential complications reliably. Ho…
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis
Santosh Sanjeev, Salwa K. Al Khatib, Mai A. Shaaban +3
Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks…
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection
Naif Alkhunaizi, Koushik Srivatsan, Faris Almalik +2
Lack of generalization to unseen domains/attacks is the Achilles heel of most face presentation attack detection (FacePAD) algorithms. Existing attempts to enhance the generalizabi…
SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification
Raza Imam, Ibrahim Almakky, Salma Alrashdi +2
Deep Learning methods have recently seen increased adoption in medical imaging applications. However, elevated vulnerabilities have been explored in recent Deep Learning solutions,…