IDEA
Digital twin research for next-generation lung cancer diagnosis, virtual treatment, and decision support.
Research Project
IDEA
Digital twin research for next-generation lung cancer diagnosis, virtual treatment, and decision support.
Overview
IDEA proposes AI-powered digital twins for next-generation lung cancer care. The project combines imaging, treatment information, data security, explainability, and ethics to support clinical decision-making for non-small cell lung cancer.
The technical programme covers radiological digital twins, virtual scans, virtual treatments, and AI methods that can support diagnosis and therapy planning while accounting for data governance and clinical usability.
Research Directions
- Radiological digital twins for lung cancer diagnosis and treatment decision support.
- Virtual scanning and virtual treatments for patient-specific clinical scenarios.
- Data interfaces, security, explainability, fairness, and ethical analysis.
Related Publications
- Beyond a single mode: GAN ensembles for diverse medical data generation2026generative AI medical imaging
- A Systematic Benchmark of GAN Architectures for MRI-to-CT SynthesisarXiv preprint arXiv:2603.13520, 2026
- XGeM: A multi-prompt foundation model for multimodal medical data generation2026foundation models generative AI multimodal learning
- Longitudinal NSCLC Treatment Progression via Multimodal Generative ModelsarXiv preprint arXiv:2603.06147, 2026
- Evaluating Vision Language Model Adaptations for Radiology Report Generation in Low-Resource Languages2025foundation models medical imaging
- Lesion-Aware Generative Artificial Intelligence for Virtual Contrast-Enhanced Mammography in Breast Cancer2025radiomics oncology generative AI
- Augmented intelligence for multimodal virtual biopsy in breast cancer using generative artificial intelligence2025oncology multimodal learning generative AI
- Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation2025foundation models medical imaging generative AI
- Benchmarking GAN-Based vs Classical Data Augmentation on Biomedical Images2025
- Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin2025medical imaging generative AI
- Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging2025multimodal learning medical imaging
- A systematic review of intermediate fusion in multimodal deep learning for biomedical applications2025multimodal learning
- Texture-Aware StarGAN for CT data harmonization2025generative AI medical imaging
- MARIA: A multimodal transformer model for incomplete healthcare data2025multimodal learning foundation models clinical prediction
- Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging2025foundation models medical imaging
- A deep learning approach for virtual contrast enhancement in Contrast Enhanced Spectral Mammography2024medical imaging generative AI
- A deep learning approach for overall survival prediction in lung cancer with missing values2024clinical prediction oncology