The Power of Transfer Learning

Learning That Adapts to New Data
Transfer learning harnesses existing AI models and adapts them using new, domain-specific data to achieve higher accuracy and reliability, especially for specialized tasks such as tuberculosis detection from AFB TB smears. This approach allows the AI to build on prior knowledge rather than starting from scratch, making it particularly well-suited to specialized medical imaging applications.
Real-World Validation — Refining AI With Data From Tzu Chi Foundation Medical Center
When Wellgen Medical incorporates new data validation—using microscopic images directly from its customer, Tzu Chi Foundation medical center in Hualien—it enables the AI to refine its knowledge base, reduce errors, and improve diagnostic performance. By validating with 1,000 fresh data samples, the system reached outstanding performance metrics, including 99% sensitivity, 99.7% specificity, and near-perfect accuracy and Cohen's Kappa scores, indicating robust reliability and minimal misclassification.
Industry Value — A Continuous Cycle of Real-World Learning
This continuous cycle of learning from new, real-world samples allows the AI to keep pace with evolving laboratory conditions, ensuring that its predictions remain trustworthy and clinically valuable. It also reflects Wellgen's dedication to customer excellence and satisfaction in adapting AI into medical practice—reinforcing the company's core mission of "AI-Assisted Diagnosis" through data-driven, continuous improvement.