Multi-modal AI for Enhanced Breast Cancer Detection
Innovative AI Solutions for Healthcare
Cutting-edge research in medical AI and explainable systems
Project Overview
- Early detection of breast cancer relies on imaging techniques like ultrasound, mammography, and MRI scans, aiding in cancer diagnosis and treatment planning. However, interpreting these images accurately poses challenges due to variability in image quality and tumor characteristics.
- Recent advancements in AI offer potential solutions, aiming to automate processes, reduce errors, and save time in healthcare. Yet, AI models often lack transparency, hindering their clinical application.
- This project seeks to address these challenges by developing deep learning models that integrate multi-modal data such as ultrasound, mammography, and MRI scans to enhance accuracy and robustness.
- By incorporating explainable AI techniques, we aim to improve transparency in model decision-making, providing insights into key features influencing predictions to enhance trustworthiness for medical professionals.
Key Innovations
Multi-modal integration: Combining ultrasound, mammography, and MRI data
Explainable AI: Transparent decision-making for clinical trust
Generalizability testing: Evaluation on unseen clinical data
Domain adaptation: Addressing data acquisition variations
Technical Approach
Multi-modal Fusion
Developing architectures to effectively combine information from different imaging modalities (ultrasound, mammography, MRI) for comprehensive analysis.
Explainable AI
Implementing techniques like attention mechanisms and saliency maps to provide interpretable explanations for model predictions.
Domain Adaptation
Employing advanced methods to ensure model robustness across different imaging devices and clinical settings.
Clinical Validation
Rigorous testing on diverse, unseen clinical datasets to evaluate real-world performance and generalizability.
Potential Impact
Improved accuracy in early breast cancer detection through comprehensive multi-modal analysis
Enhanced clinical adoption through transparent and explainable AI decision-making
Reduced diagnostic errors and variability in interpretation across different healthcare settings
Potential for integration into clinical workflows to support radiologists and improve patient outcomes
Transformer-based Deep Learning for Rapid Stroke Diagnosis
Qatar-Japan Research Collaboration (QJRC) program (Cycle-2), Qatar
Project Summary
- Strokes are a leading cause of mortality worldwide, and rapid response to stroke recognition is critical to preserving a patient's life and minimizing brain damage. Qatar is one of the countries that experiences a high incidence of stroke due to hypertension and diabetes.
- Magnetic Resonance Imaging (MRI) and or Computed Tomography (CT) imaging protocols are used in stroke identification, with MRI being the most commonly used diagnostic method. However, the quality of the imaging protocol dependents on scanning time, and the interpretation of the scan requires the expertise of a radiologist, which may not always be available.
- This project aims at the development of a machine learning-based ischemic stroke characterization model through collaboration between Qatar University and University of Hyogo.
- The project's objectives include establishing an international network, knowledge exchange, and supporting researchers and students affiliated to collaborated teams. The research is expected to have a significant impact on improving skills of young researchers.
Key Features
AI-powered stroke diagnosis
International research collaboration
Focus on rapid response
Researcher skill development
Collaborating Institutions
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