The Smart Computing Laboratory in the Institute of Digital Anti-Aging Healthcare at Inje University stands at the forefront of innovation in digital healthcare, merging advanced computational techniques with real-world healthcare applications. Led by Professor Hee-Cheol Kim, the lab is committed to addressing many of the most pressing challenges in healthcare through the application of cutting-edge technologies such as Machine Learning, Deep Learning, Reinforcement Learning, Computer Vision, Natural Language Processing (NLP), Generative AI, and Blockchain. Our diverse team comprises more than 40 researchers from various countries, including Bangladesh, India, Pakistan, Uzbekistan, Vietnam, Myanmar, Cameroon, Uganda, Europe, America, and Korea. This multicultural environment fosters a rich exchange of ideas and perspectives, driving innovative solutions and global collaborations. Furthermore, we have published several articles and conference papers internationally, contributing to the global knowledge base and advancing the field of digital healthcare.
Innovation in healthcare goes beyond the introduction of new technologies; it is about creating systems that empower both patients and healthcare providers. By integrating advanced tools with compassionate care, we can improve not only the efficiency of treatments but also the overall experience and outcomes for patients.
Our research focuses on advancing the analysis and interpretation of Whole Slide Imaging (WSI) and Digital Pathology Images, transformative technologies in modern medical diagnostics. By leveraging artificial intelligence (AI) and machine learning, we aim to enhance processes such as nuclei segmentation, feature extraction, and classification, improving diagnostic accuracy and reducing processing time. These innovations are particularly impactful in the early detection, diagnosis, and treatment planning for diseases like cancer and neurodegenerative disorders, enabling personalized and precise care. We address challenges like data scarcity and variability in staining protocols using advanced preprocessing and augmentation techniques. By integrating explainable AI, we provide clinicians with clear, interpretable insights, fostering trust in AI-driven systems. Our work is paving the way for revolutionizing disease detection and management, offering new hope to patients, and advancing precision medicine.