SEARCH
Rethinking the labor-intensive process of medical AI labeling.
Labeling has long been used extensively in building AI/ML algorithms. The idea is to apply the minds and knowledge of professionals to teach AI/ML models to learn from experts. In medical AI specifically, labeling typically involves identifying and marking the "Area of Interest" (AoI) as "positive" for the model to learn from.
This process has relied on an army of pathologists dedicating their time and effort to this labor-intensive work, carefully reviewing and annotating countless images by hand. Wellgen deeply appreciates this contribution, while recognizing that this traditional approach carries real limitations in scalability and efficiency as the demand for AI-ready medical data continues to grow.
依據歐盟施行的個人資料保護法，我們致力於保護您的個人資料並提供您對個人資料的掌握。
按一下「全部接受」，代表您允許我們置放 Cookie 來提升您在本網站上的使用體驗、協助我們分析網站效能和使用狀況，以及讓我們投放相關聯的行銷內容。您可以在下方管理 Cookie 設定。 按一下「同意」即代表您同意採用目前的設定，更多資訊請瀏覽 隱私權聲明。
