Machine Learning for Multimodal Healthcare Data

Name: Machine Learning for Multimodal Healthcare Data | First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings
Brand: Springer
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First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings

Andreas K. Maier Julia A. Schnabel Pallavi Tiwari Oliver Stegle(Herausgeber*in)

Springer (Verlag)

Erschienen am 25. November 2023

X, 190 Seiten

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978-3-031-47679-2 (ISBN)

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Inhalt

Intro
Preface
Organization
Contents
Death Prediction by Race in Colorectal Cancer Patients Using Machine Learning Approaches
1 Introduction
2 Materials and Methods
2.1 Data
2.2 Pre-processing
2.3 Machine Learning Methods
3 Results and Discussion
4 Conclusions
References
Neural Graph Revealers
1 Introduction
2 Related Methods
3 Neural Graph Revealers
3.1 Representation
3.2 Optimization
3.3 Modeling Multi-modal Data
3.4 Representation as a Probabilistic Graphical Model
4 Experiments
4.1 Learning Gaussian Graphical Models
4.2 Infant Mortality Data Analysis
5 Conclusions, Discussion and Future Work
6 NGR Design Choices
References
Multi-modal Biomarker Extraction Framework for Therapy Monitoring of Social Anxiety and Depression Using Audio and Video
1 Introduction
2 Data
3 Audio Biomarkers
3.1 Prosody
3.2 PAD Model
4 Video Biomarkers
4.1 Face Emotion Recognition
5 Results
5.1 Audio Biomarkers
5.2 Video Biomarkers
6 Discussion
7 Conclusion
References
RobustSsF: Robust Missing Modality Brain Tumor Segmentation with Self-supervised Learning-Based Scenario-Specific Fusion
1 Introduction
2 Method
2.1 Overall Architecture
2.2 Self-supervised Learning-Based Scenario-Specific Fusion
2.3 Lifelong Learning Strategy (LLS)
2.4 Total Loss
3 Experimental Results
3.1 Experimental Setup
3.2 Results and Analysis
4 Conclusion
References
Semi-supervised Cooperative Learning for Multiomics Data Fusion
1 Introduction
2 Approach
2.1 Cooperative Learning
2.2 Semi-supervised Cooperative Learning
3 Experiments
3.1 Simulated Studies
3.2 Real Data Example
4 Conclusion
References
Exploiting Partial Common Information Microstructure for Multi-modal Brain Tumor Segmentation
1 Introduction
2 Related Works
2.1 Medical Image Segmentation Approaches
2.2 HGR Correlation in Multi-modal Learning
3 Background
3.1 Brain Tumor Segmentation
3.2 HGR Maximal Correlation
3.3 Soft-HGR
4 Identifying Partial Common Information
4.1 Masked Correlation Maximization
4.2 Learning Microstructure via PCI-Mask Update
5 System Design
5.1 Model Learning
5.2 Model Design
6 Experiments
6.1 Datasets
6.2 Data Preprocessing and Environmental Setup
6.3 Evaluation Metrics
6.4 Main Results
6.5 Ablation Experiments
7 Conclusion
A Proof of Theorem 1
B Proof of Lemma 1
C Algorithms
C.1 Masked Maximal Correlation Loss
C.2 Routine: Truncation Function
D Supplementary Experiments
D.1 Implementation Details and Hyperparameters
D.2 Experimental Results on BraTS-2015 Dataset
References
Multimodal LLMs for Health Grounded in Individual-Specific Data
1 Introduction
2 Methods
2.1 LLMs with Tabular Data
2.2 Multimodal LLMs for Health: HeLM
2.3 UK Biobank Dataset Preparation
3 Experimental Results
3.1 Quantifying Disease Risk Using Zero-Shot, Few-Shot, and Soft-Prompt Tuning
3.2 Encoding Quantitative Data Using HeLM
3.3 Estimating Asthma Risk Using Multiple Modalities
3.4 Using HeLM for Out-of-Distribution Traits
3.5 Natural Language Generation
4 Discussion
References
Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders
1 Introduction
2 Methods
2.1 Network Architecture, Training
2.2 Network Architecture, Inference
3 Results
3.1 Training, Linked Autoencoder
3.2 Inference, Simulated Data
3.3 Inference, Measured Data
4 Discussion and Conclusion
References
HOOREX: Higher Order Optimizers for 3D Recovery from X-Ray Images
1 Introduction
2 Methods
2.1 BOSS Model
2.2 Regression Network
2.3 Second Order In-the-Loop Optimization
2.4 Loss Functions
3 Experiments and Results
3.1 Data
3.2 Experimental Setup
3.3 Results
4 Discussion and Conclusion
References
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
1 Introduction
2 Related Work
3 GastroVision
3.1 Dataset Details
3.2 Dataset Acquisition, Collection and Construction
3.3 Suggested Metrics
4 Experiments and Results
4.1 Implementation Details
4.2 Technical Validation
4.3 Limitation of the Dataset
5 Conclusion
References
MaxCorrMGNN: A Multi-graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
1 Introduction
1.1 Related Works
1.2 Our Contributions
2 MaxCorrMGNN Formulation for Multimodal Fusion
2.1 Patient-Modality Multi-layered Graph
2.2 HGR Maximal Correlations for Latent Multi-graph Learning
2.3 Multi-graph Neural Network
2.4 End-to-End Learning Through Task Supervision
3 Experiments and Results
3.1 Data and Preprocessing
3.2 Evaluation Metrics
3.3 Baseline Comparisons
3.4 Outcome Prediction Performance
4 Discussion
5 Conclusion
References
SIM-CNN: Self-supervised Individualized Multimodal Learning for Stress Prediction on Nurses Using Biosignals
1 Introduction
2 Related Work
2.1 Time Series Prediction with 1D CNNs
2.2 Multimodal Fusion and Machine Learning
2.3 Personalized Patient-Specific Methods
2.4 Self-supervised Learning
3 Methods
3.1 Dataset Description
3.2 Data Preprocessing
3.3 Model Architecture
3.4 SIM-CNN
4 Results
4.1 Evaluation Metrics
4.2 Model Performance
5 Discussion
5.1 Limitations and Future Work
6 Conclusion
References
InterSynth: A Semi-Synthetic Framework for Benchmarking Prescriptive Inference from Observational Data
1 Introduction
1.1 Evaluating the Fidelity of Prescriptive Inference
2 Problem Setup
2.1 Assumptions
2.2 Ground Truth Modelling
3 Multimodal Prescriptive Ground Truth Modelling Using Neuroimaging Data
3.1 Observed Clinical Deficit Modelling
3.2 Unobserved Treatment Target Modelling
3.3 Prescriptive Ground Truth
4 Defining Observational Data Conditions
4.1 Modelling Assignment Bias
4.2 Modelling Interventional Effects
4.3 Ground Truth Compilation
5 Lesion Phenotype Modelling
5.1 Raw Lesion Data
5.2 Disconnectome Representations
5.3 Succinct Latent Representations
5.4 Atlas-Based Representations
6 End-to-End Framework Functionality
7 Discussion
7.1 Observational Vs RCT Ground Truths
8 Conclusion
References
Author Index

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