MBZUAI's BioMedIA lab, led by Mohammad Yaqub, is developing AI solutions for healthcare challenges in cardiology, pulmonology, and oncology using computer vision. Yaqub's previous research analyzed fetal ultrasound images to correlate bone development with maternal vitamin D levels. The lab is now applying image analysis to improve the treatment of head and neck cancer using PET and CT scans. Why it matters: This research demonstrates the potential of AI and computer vision to improve diagnostic accuracy and accessibility of healthcare in the region and beyond.
Researchers propose a universal anatomical embedding (UAE) framework for medical image analysis to learn appearance, semantic, and cross-modality anatomical embeddings. UAE incorporates semantic embedding learning with prototypical contrastive loss, a fixed-point-based matching strategy, and an iterative approach for cross-modality embedding learning. The framework was evaluated on landmark detection, lesion tracking and CT-MRI registration tasks, outperforming existing state-of-the-art methods.
The study compares deep learning models trained via transfer learning from ImageNet (TII-models) against those trained solely on medical images (LMI-models) for disease segmentation. Results show that combining outputs from both model types can improve segmentation performance by up to 10% in certain scenarios. A repository of models, code, and over 10,000 medical images is available on GitHub to facilitate further research.
A senior lecturer at the University of New South Wales discussed the use of AI to improve early prognosis and personalized treatment plans for neurodegenerative diseases, cardiovascular imaging and multiomics. The lecture highlighted the potential of AI algorithms to detect subtle changes at early stages through advanced multiomics techniques and medical imaging analysis. The speaker has expertise in analyzing medical images and has collaborated with medical professionals to develop AI tools for diagnosis of cancer, neurodegenerative disease, and heart disease. Why it matters: AI-driven prognosis and treatment planning promises earlier intervention and improved outcomes for challenging diseases in the region.
An MBZUAI team developed a self-ensembling vision transformer to enhance the security of AI in medical imaging. The model aims to protect patient anonymity and ensure the validity of medical image analysis. It addresses vulnerabilities where AI systems can be manipulated, leading to misinterpretations with potentially harmful consequences in healthcare. Why it matters: This research is crucial for building trust and enabling the safe deployment of AI in sensitive medical applications, protecting against fraud and ensuring patient safety.
Pascal Fua from EPFL gave a talk at MBZUAI on physics-based deep learning for medical imaging. The talk covered how self-supervision and knowledge of human anatomy and physics can improve deep learning algorithms when training data is limited. Applications discussed included endoscopic heart surgery, colonoscopy, and intubation. Why it matters: This highlights the growing importance of domain knowledge and self-supervision in overcoming data scarcity challenges for AI in healthcare applications within the region.
This survey paper reviews recent literature on continual learning in medical imaging, addressing challenges like catastrophic forgetting and distribution shifts. It covers classification, segmentation, detection, and other tasks, while providing a taxonomy of studies and identifying challenges. The authors also maintain a GitHub repository to keep the survey up-to-date with the latest research.