Machine Learning for Multimodal Healthcare Data

Chapter 1 Death Prediction by Race in Colorectal Cancer Patients Using Machine Learning Approaches

Chapter 2 Neural Graph Revealers

Chapter 3 Multi-modal Biomarker Extraction Framework for Therapy Monitoring of Social Anxiety and Depression Using Audio and Video

Chapter 4 RobustSsF: Robust Missing Modality Brain Tumor Segmentation with Self-supervised Learning-Based Scenario-Specific Fusion

Chapter 5 Semi-supervised Cooperative Learning for Multiomics Data Fusion

Chapter 6 Exploiting Partial Common Information Microstructure for Multi-modal Brain Tumor Segmentation

Chapter 7 Multimodal LLMs for Health Grounded in Individual-Specific Data

Chapter 8 Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders

Chapter 9 HOOREX: Higher Order Optimizers for 3D Recovery from X-Ray Images

Chapter 10 GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

Chapter 11 MaxCorrMGNN: A Multi-graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction

Chapter 12 SIM-CNN: Self-supervised Individualized Multimodal Learning for Stress Prediction on Nurses Using Biosignals

Chapter 13 InterSynth: A Semi-Synthetic Framework for Benchmarking Prescriptive Inference from Observational Data