Modelling the Neonatal Brain Development Using Implicit Neural Representations.- Attention Based Features Fusion Emotion Guided fNIRS Classification Network for Prenatal Depression Recognition.- Spectral Graph Sample Weighting for Interpretable Sub cohort Analysis in Predictive Models for Neuroimaging.- RCT Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence.- Gene to Image Decoding Brain Images from Genetics via Latent Diffusion Models.- Physics Guided Multi View Graph Neural Network for Schizophrenia Classification via Structural Functional Coupling.- Automated Patient Specific Pneumoperitoneum Model Reconstruction for Surgical Navigation Systems in Distal Gastrectomy.- MNA net Multimodal Neuroimaging Attention based Architecture for Cognitive Decline Prediction.- Improving Brain MRI Segmentation with Multi Stage Deep Domain Unlearning.- DynGNN Dynamic Memory enhanced Generative GNNs for Predicting Temporal Brain Connectivity.- Strongly Topology preserving GNNs for Brain Graph Super resolution.- Generative Hypergraph Neural Network for Multiview Brain Connectivity Fusion.- Identifying brain ageing trajectories using variational autoencoders with regression model in neuroimaging data stratified by sex and validated against dementia related risk factors.- Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction.- Self-Supervised Contrastive Learning for Consistent Few Shot Image Representations.- Neurocognitive Latent Space Regularization for Multi Label Diagnosis from MRI.- Segmentation of Brain Metastases in MRI A Two Stage Deep Learning Approach with Modality Impact Study.