Seeing Unseen: Discover Novel Biomedical Concepts via GeometryConstrained Probabilistic Modeling

arXiv:2403.01053v1 Announce Type: new
Abstract: Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing stream of research data collection, it would be appealing to autonomously explore patterns and insights from observational data for discovering novel classes of phenotypes and concepts. However, in the biomedical domain, there are several challenges inherently presented in the cumulated data which hamper the progress of novel class discovery. The non-i.i.d. data distribution …

abstract arxiv biomedical collection concepts cs.ai cs.cv cs.lg data data collection data-driven discovery explore insights machine machine learning modeling nature novel patterns practice probabilistic modeling research scientific discovery type via