M. Akhloufi

Variational Autoencoders for Medical Image Segmentation: A Review

Medical image segmentation plays an important role in healthcare because it transforms raw scans into useful information for diagnosis and treatment planning. Despite the strong performance of deep learning, most supervised segmentation methods require large amounts of pixel-level annotations and may become less reliab...

TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs

TrajFusionNet+, a novel transformer-based model for pedestrian crossing intention prediction that combines sequential and visual representations of pedestrian trajectory with a graph-based representation of the scene context in order to predict pedestrian crossing intention, is introduced.

Lightweight Deep Learning Models for Real Time Wildfire Detection

MultiCardioNet: A Multimodal Deep Learning Model for Early Cardiovascular Deterioration Prediction in ICU Patients

MultiCardioNet, a multimodal deep learning framework for early prediction of a composite ICU deterioration endpoint using MIMIC-IV data, suggests that the three modalities provide complementary information for this composite deterioration endpoint in the internal test setting.

Task-Guided CycleGAN for Domain-Adapted Coastal Boundary Segmentation in Remote Sensing Images

It is demonstrated that task-guided style transfer can improve cross-domain coastal boundary segmentation while maintaining a static segmentation model, making it a promising approach for long-term coastal monitoring across heterogeneous remote sensing datasets.

Generative AI in Neuroimaging: Advancing Brain MRI Analysis and Interpretation

Overall, the reviewed literature demonstrates that generative AI has substantial potential to improve brain MRI analysis through realistic data generation, enhanced image quality, and more informative feature representations.