The Challenge
Medical imaging plays a critical role in cancer diagnosis and treatment planning. However, interpreting these images is complex, time-consuming, and subject to variability between radiologists. Traditional image analysis often fails to capture subtle patterns and features that could provide valuable diagnostic and prognostic information.
Furthermore, the increasing volume and complexity of medical imaging data—including multiple modalities such as CT, MRI, PET, and ultrasound—create challenges for clinical workflows and limit the extraction of comprehensive insights from this rich data source.
Our Approach
Our research leverages advanced deep learning architectures specifically designed for medical image analysis. We're developing AI systems that can:
- Detect and segment tumors with high precision across multiple imaging modalities
- Identify radiomic features that correlate with genetic profiles and treatment outcomes
- Reconstruct and enhance medical images to improve diagnostic quality
- Track subtle changes in tumor characteristics over time to evaluate treatment response
- Generate synthetic images to augment training data and improve model performance
Our models incorporate domain-specific knowledge in oncology and radiology, and are designed to provide interpretable results that can support clinical decision-making.
Multimodal Fusion
Integrating information from various imaging modalities (CT, MRI, PET) to provide a more comprehensive view of tumor characteristics and environment.
Radiomics & Radiogenomics
Extracting quantitative features from medical images and correlating them with genomic data to uncover new biomarkers and predictive signatures.
Image Reconstruction
Applying deep learning techniques to improve image quality, reduce artifacts, and enable lower-dose imaging protocols without sacrificing diagnostic information.
Real-time Analysis
Developing systems capable of providing instantaneous analysis and feedback during image acquisition or interventional procedures.
Current Research Projects
4D Tumor Evolution Mapping
We're developing an AI system that integrates temporal sequences of multi-parametric MRI to create dynamic, four-dimensional maps of tumor evolution. This project enables visualization of tumor growth patterns, vascular changes, and treatment effects over time, providing insights into cancer progression mechanisms and response to therapy.
AI-Enhanced Interventional Radiology
This project focuses on real-time guidance systems for interventional procedures such as tumor ablations and biopsies. Our deep learning models provide instant feedback during procedures, helping to optimize targeting accuracy, minimize damage to surrounding healthy tissue, and improve procedural outcomes.
Low-Field MRI Enhancement
We're applying generative models to enhance images from low-field MRI systems, which are more accessible and affordable than high-field systems. This project aims to democratize access to high-quality imaging, particularly in resource-constrained settings, without compromising diagnostic accuracy.
Whole-Body Tumor Burden Assessment
Our team is developing algorithms for automated quantification of total tumor burden from whole-body imaging studies. This approach provides a more comprehensive assessment of disease status than conventional single-lesion measurements, enabling better treatment planning and response monitoring.
Technical Innovations
Our research incorporates several cutting-edge technical approaches:
- Self-supervised learning: Leveraging vast amounts of unlabeled medical imaging data to pre-train models that can then be fine-tuned with limited labeled data
- Few-shot and zero-shot learning: Developing models that can generalize to new cancer types or imaging protocols with minimal or no additional training
- Uncertainty quantification: Implementing techniques that provide confidence estimates with predictions, critical for clinical decision-making
- Federated learning: Creating collaborative training frameworks that enable model improvement across institutions without sharing sensitive patient data
- Physics-informed neural networks: Incorporating physical models of imaging processes to improve reconstruction and analysis
Clinical Applications
Our medical imaging research addresses key clinical needs across the cancer care continuum:
Screening & Detection
Enhancing cancer screening programs through automated detection of suspicious findings, risk stratification, and prioritization of cases for radiologist review.
Diagnosis & Staging
Providing detailed characterization of tumors and assessment of disease extent to guide treatment planning and prognostic assessment.
Treatment Planning
Supporting precise targeting for radiation therapy, surgical planning, and optimal intervention strategies based on comprehensive tumor mapping.
Response Assessment
Quantifying changes in tumor characteristics over time to evaluate treatment efficacy and guide adaptive therapy approaches.
Collaborations and Partnerships
Our medical imaging research thrives on collaborative partnerships. We're actively seeking to work with:
Radiology Departments
For access to diverse imaging data and clinical expertise to guide our algorithm development
Medical Imaging Companies
To implement our algorithms in commercial imaging platforms and accelerate clinical adoption
Cloud Computing Providers
To develop scalable infrastructure for processing and analyzing large medical imaging datasets
Research Background
This research area contributes to the growing body of knowledge in AI-powered cancer research. We're currently developing foundational work in this space.
Related Research Areas
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