Synthetic Data Generation | Rudiment

The Challenge

While artificial intelligence holds tremendous promise for cancer research and care, its development is often hampered by critical data challenges. Real patient data is subject to strict privacy regulations that limit sharing and access, many cancer types and subtypes have insufficient data due to their rarity, and existing datasets often contain biases that can lead to AI systems that perform poorly on underrepresented populations.

Additionally, creating comprehensive datasets that include longitudinal information, multimodal data (imaging, genomics, clinical, etc.), and outcomes across diverse populations is extraordinarily difficult. These data limitations significantly constrain the development, validation, and generalizability of AI systems for cancer applications, ultimately slowing progress toward better cancer care.

Our Approach

We aim to address these challenges through the development of advanced synthetic data generation approaches. Our multifaceted research agenda includes:

Developing generative models that can create realistic yet privacy-preserving synthetic patient data across multiple modalities (clinical, imaging, genomic, etc.)
Creating methods to augment existing datasets by generating additional samples for rare cancer types, subtypes, or underrepresented patient populations
Building simulation environments that can model disease progression, treatment responses, and outcomes under different scenarios
Designing novel evaluation frameworks to assess the fidelity, utility, and privacy guarantees of synthetic data
Exploring approaches to create synthetic multimodal datasets that capture the complex relationships between different data types in oncology

Privacy-Preserving Synthesis

Creating synthetic data with formal privacy guarantees that enable sharing and collaboration without compromising patient confidentiality.

Data Augmentation

Generating additional samples for rare cancer types and underrepresented populations to enable more robust and equitable AI development.

Disease Simulation

Building computational models that simulate cancer progression and treatment responses to generate virtual patient cohorts for research.

Multimodal Synthesis

Creating synthetic datasets that capture the complex relationships between different data types such as imaging, genomics, and clinical variables.

Potential Research Projects

Privacy-Preserving Clinical Oncology Datasets

We could develop advanced generative models that create synthetic electronic health record (EHR) data for cancer patients, preserving the complex relationships between diagnoses, treatments, lab values, medications, and outcomes while providing formal privacy guarantees. These synthetic datasets could be freely shared among researchers without the privacy concerns and regulatory hurdles associated with real patient data, potentially accelerating collaborative research across institutions.

Synthetic Medical Imaging for Rare Cancers

This project would focus on generating synthetic medical images (CT, MRI, pathology) for rare cancer types and presentations. By creating realistic but entirely artificial images with corresponding annotations, we could significantly expand the available training data for these underrepresented conditions. The synthetic images would maintain the distinctive radiological or pathological features that characterize these rare cancers while introducing natural variations to support robust model training.

Population-Representative Synthetic Cohorts

We aim to develop methods for generating synthetic patient cohorts that more accurately represent diverse populations, including those often underrepresented in real datasets. By carefully modeling demographic factors, comorbidities, socioeconomic variables, and their relationships to cancer characteristics and outcomes, these synthetic cohorts could enable the development of more equitable AI models. Researchers could use these datasets to test and improve the performance of their algorithms across different population groups.

Integrated Cancer Progression Simulator

This ambitious project would create a computational framework that simulates the natural history of different cancer types, from initiation through progression, metastasis, and response to various treatments. By incorporating biological mechanisms, patient characteristics, and treatment effects, this simulator could generate longitudinal synthetic data for virtual patient cohorts under different scenarios. Such a platform could support the exploration of treatment strategies, enable in silico clinical trials, and generate synthetic datasets with known ground truth for algorithm development.

Technical Innovations

Our synthetic data research would leverage several cutting-edge technical approaches:

Generative adversarial networks (GANs): Advanced architectures for generating highly realistic synthetic data across various modalities
Diffusion models: State-of-the-art generative approaches that excel at creating high-fidelity images and structured data
Differential privacy: Mathematical frameworks that provide formal privacy guarantees for synthetic data generation
Causal modeling: Techniques to capture and preserve cause-and-effect relationships in synthetic datasets
Agent-based simulation: Methods to model complex interactions between virtual patients, treatments, and biological processes
Deep generative models with constraints: Approaches that enforce medical validity and plausibility in synthetic data

Data Modality Focus

Our research would address synthetic data generation across multiple important data types in oncology:

Clinical Data

Generating synthetic electronic health records, including diagnoses, treatments, procedures, medications, lab values, and clinical outcomes.

Medical Imaging

Creating synthetic radiological images (CT, MRI, PET) and pathology slides that maintain clinically relevant features and relationships.

Genomic Data

Synthesizing realistic genomic profiles including mutations, expression patterns, and other molecular characteristics of different cancer types.

Application Areas

Synthetic data can address key challenges across the cancer research spectrum:

Algorithm Development

Providing abundant training data for AI model development, particularly for applications requiring large diverse datasets or rare cancer types.

Privacy-Preserving Collaboration

Enabling data sharing and collaborative research across institutions without transferring sensitive real patient information.

Fairness & Equity

Creating representative datasets that include adequate samples from diverse populations to reduce algorithmic bias.

In Silico Trials

Supporting virtual clinical trials and treatment simulations to accelerate therapeutic development and optimize trial design.

Evaluation Framework

Assessing synthetic data quality requires evaluation across multiple dimensions:

Fidelity: How closely the synthetic data resembles real data in terms of statistical properties and distributions
Utility: Whether models trained on synthetic data perform similarly to those trained on real data when applied to real-world tasks
Privacy: Ensuring the synthetic data doesn't leak sensitive information about real patients in the original dataset
Clinical validity: Whether the synthetic data maintains medically plausible relationships between variables
Diversity: How well the synthetic data captures the full spectrum of variation present in the population of interest
Causal consistency: Whether intervention effects in the synthetic data match those observed in real clinical scenarios

Future Directions

As our research progresses, we plan to explore several exciting directions:

Developing methods for generating synthetic data that preserves causal relationships, enabling more reliable investigation of treatment effects
Creating frameworks for combining real and synthetic data in optimal ways to maximize model performance while maintaining privacy
Building comprehensive digital twins of cancer patients that integrate multiple data modalities and simulate disease trajectories
Exploring synthetic data approaches for emerging data types such as spatial transcriptomics, single-cell sequencing, and wearable device data
Establishing open synthetic datasets for benchmarking and accelerating research across the oncology AI community

Collaborations and Partnerships

We would be interested in exploring partnerships with:

Data Science Centers

To develop and validate novel synthetic data generation methods across diverse oncology applications

Privacy Technology Experts

To ensure robust privacy guarantees for synthetic data approaches while maximizing utility

Cancer Research Consortia

To identify high-priority use cases where synthetic data can address critical research bottlenecks

AI for Synthetic Data Generation