Research Field

Medical data exists in a wide variety of forms, including proteins and molecules (graphs), genomes and transcriptomes (natural language), medical images (multidimensional images), spectrums (frequency signals), heart rate and metabolomics variations (time-series signals), patient metadata and information on interactions between substances (multidimensional matrices). For personalized precision diagnostics, a comprehensive analysis of various modalities for individual patients is essential, and, above all, the basis for diagnosis must be presented clearly.

Our laboratory is dedicated to the precise processing of such complex multimodal medical data and developing advanced foundational generative AI technologies that produce results intuitively understandable by the general public. We welcome the participation and collaboration of all researchers interested in developing cutting-edge multimodal AI technologies that are grounded in theoretical evidence and capable of validating their effectiveness in real clinical settings.

Multimodal AI (medical images/graphs/natural language/frequency/time-series/matrix data, etc.)
Generative explainable AI
Unsupervised anomaly detection AI
Causal/biomarker detection AI
Reliability assessment AI
Bias removal AI
3D/4D/time-series/natural language signal restoration AI

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Keyword

Bioinformatics, AI, Medical Imaging