Predictive Intelligence in Medicine

TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data.- Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study.- Adaptive Neuro-Fuzzy Inference System-based Chaotic Swarm Intelligence Hybrid Model for Recognition of Mild Cognitive Impairment from Resting-state fMRI.- Deep Learning via Fused Bidirectional Attention Stacked Long Short-term Memory for Obsessive-Compulsive Disorder Diagnosis and Risk Screening.- Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning.- Predicting Response to the Antidepressant Bupropion using Pretreatment fMRI.- Progressive Infant Brain Connectivity Evolution Prediction from Neonatal MRI using Bidirectionally Supervised Sample Selection.- Computed Tomography Image-Based Deep Survival Regression for Metastatic Colorectal Cancer using a Non-Proportional Hazards Model.- 7 years of Developing Seed Techniques for Alzheimer's Disease Diagnosis using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis.- Generative Adversarial Irregularity Detection in Mammography Images.- Hierarchical Adversarial Connectomic Domain Alignment for Target Brain Graph Prediction and Classification From a Source Graph.- Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-Resolution Neighborhoods.- Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks.- Prediction of Clinical Scores for Subjective Cognitive Decline and Mild Cognitive Impairment.- Diagnosis of Parkinsons Disease in Genetic Cohort Patients via Stage-wise Hierarchical Deep Polynomial Ensemble learning.- Automatic Detection of Bowel Disease with Residual Networks.- Support Vector based Autoregressive Mixed Models of Longitudinal Brain Changes and Corresponding Genetics in Alzheimers Disease.- Treatment Response Prediction of Hepatocellular Carcinoma Patients from Abdominal CT Images with Deep Convolutional Neural Networks.