Research Areas
Our team conducts research on deep learning-based medical image analysis, focusing on MRI and related modalities.
Our team applies deep learning methods to analyze EEG signals in Brain-Computer Interfaces (BCIs). We aim to provide personalized solutions for individual users, while also working on frameworks that have broader applicability.
Our team conduct comprehensive research into a range of complex neurological disorders, working to advance the field of diagnosis techniques using various deep learning techniques.
To gain a deeper understanding of brain function, our team aimed to enhance the accuracy of calcium signals by refining the processing of calcium imaging data. We employ deep learning techniques for the processing of calcium imaging data.
Our team excels in the field of Molecular AI, unraveling the intricacies of molecular behaviors through innovative research. We are now pioneering research in Molecular Optimization, aiming to automate the refinement of molecular structure.
Our team leverages reinforcement learning for personalized medical decision-making, continuous learning, and research in areas like ROI and EEG-based medical traits, with a focus on enhancing care and patient outcomes.
Our team focuses on multimodal learning in Medical AI by developing vision-language models that integrate imaging, signals, clinical text, and knowledge to tackle real-world clinical tasks.
Our team develops robust semi-supervised segmentation frameworks that address annotation scarcity in medical imaging, aiming for practical deployment in real-world clinical AI settings.