arXiv ·
A new brain tumor segmentation method based on convolutional neural networks is proposed for the BraTS-GoAT challenge. The method employs the MedNeXt architecture and model ensembling to segment tumors in brain MRI scans from diverse populations. Experiments on the unseen validation set demonstrate promising results with an average DSC of 85.54%.
KAUST ·
KAUST's 2020 Winter Enrichment Program (WEP) focused on 'Personalized Medicine' with lectures and workshops from international and local speakers. Topics ranged from health management technology to digital health, encompassing various disciplines at KAUST. HRH Dr. Maha Bint Mishari AlSaud and Rene Frydman were among the keynote speakers. Why it matters: The program highlights KAUST's commitment to advancing precision medicine and fostering interdisciplinary collaboration in healthcare innovation within the Kingdom.
arXiv ·
The paper introduces MedNNS, a neural network search framework designed for medical imaging, addressing challenges in architecture selection and weight initialization. MedNNS constructs a meta-space encoding datasets and models based on their performance using a Supernetwork-based approach, expanding the model zoo size by 51x. The framework incorporates rank loss and Fréchet Inception Distance (FID) loss to capture inter-model and inter-dataset relationships, improving alignment in the meta-space and outperforming ImageNet pre-trained DL models and SOTA NAS methods.
MBZUAI ·
MBZUAI researchers are introducing MedNNS, a system to be presented at MICCAI 2025, that recommends the best AI architecture and initialization for medical imaging tasks. MedNNS addresses the challenge of inefficient trial-and-error in building medical imaging AI by reframing model selection as a retrieval problem. The system employs a Once-For-All ResNet-like model and a learned meta-space of 720k model-dataset pairs, using dataset embeddings to predict optimal model performance. Why it matters: By automating model selection, MedNNS promises to significantly reduce the time and resources required to develop effective AI solutions for healthcare, particularly in medical imaging.
KAUST ·
The third cohort of the NextEra startup bootcamp at KAUST has concluded, featuring 16 Saudi deep tech startups. The bootcamp, a collaboration between KAUST and the National Technology Development Program (NTDP), focuses on transforming industries aligned with Saudi Arabia's national R&D priorities. Participating startups span sectors like EdTech (Bites), cybersecurity (Nua), and healthcare (Haven Scientific, HistoApp, Labayh). Why it matters: The NextEra program is fostering a local deep tech ecosystem, driving innovation and investment in areas critical to the Kingdom's Vision 2030 goals.
Kuwait AI ·
No content was provided for this article. Therefore, a factual summary describing specific events or details cannot be generated. The title, 'Physician partners on the path of innovation', suggests a topic related to medical collaboration and advancements. Why it matters: Without the article's content, the specific significance of this potential news for the Middle East AI landscape or the healthcare sector cannot be assessed.
MBZUAI ·
Pietro Liò from the University of Cambridge will discuss geometric deep learning techniques for building a digital patient twin using graph and hypergraph representation learning. The talk will focus on integrating Computational Biology and Deep Learning, considering physiological, clinical, and molecular variables. He will also cover explainable methodologies for clinicians and protein design using diffusion models. Why it matters: This highlights the growing interest in applying advanced AI techniques like geometric deep learning and diffusion models to healthcare challenges in the region, particularly for personalized medicine.
KAUST ·
KAUST Ph.D. student Khalil Moussi won two awards at the IEEE International Conference on Nano/Micro Engineered and Molecular Systems for his research on a miniaturized drug delivery system. The system, developed in collaboration with KAIMRC, uses 3D printing and wireless power to deliver drugs for coronary artery disease treatment. The device features an electrochemical micro-pump, a 3D printed reservoir with microneedles, and a wireless powering unit, allowing customization for various in vivo drug delivery applications. Why it matters: This recognition highlights KAUST's contributions to biomedical engineering and its potential to develop innovative solutions for critical healthcare challenges in the region and beyond.