Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling

Machine learning holds tremendous promise for trans-forming 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 obser-vational data for discovering novel classes of phenotypes and concepts. However, in the biomedical domain, there are several challenges inherently presented in the cumu-lated data which hamper the progress of novel class dis-covery. The non-i.i.d. data distribution accompanied by the severe imbalance among different groups of classes es-sentially leads to ambiguous and biased semantic represen-tations. In this work, we present a geometry-constrained probabilistic modeling treatment to resolve the identified is-sues. First, we propose to parameterize the approximated posterior of instance embedding as a marginal von Mises-Fisher distribution to account for the interference of distri-butional latent bias. Then, we incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space, which in turn min-imizes the uncontrollable risk for unknown class learning and structuring. Furthermore, a spectral graph-theoretic method is devised to estimate the number of potential novel classes. It inherits two intriguing merits compared to exis-tent approaches, namely high computational efficiency and flexibility for taxonomy-adaptive estimation. Extensive ex-periments across various biomedical scenarios substantiate the effectiveness and general applicability of our method.