Summary of Seeing Unseen: Discover Novel Biomedical Concepts Via Geometry-constrained Probabilistic Modeling, by Jianan Fan et al.
Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling
by Jianan Fan, Dongnan Liu, Hang Chang, Heng Huang, Mei Chen, Weidong Cai
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach is proposed to address the challenges in discovering novel classes of phenotypes and concepts from observational data in the biomedical domain. The existing methods are hindered by non-i.i.d. data distribution and class imbalance, leading to ambiguous semantic representations. To resolve this issue, a geometry-constrained probabilistic modeling treatment is introduced. This method parameterizes the approximated posterior of instance embedding as a marginal von Mises-Fisher distribution to account for latent bias, and incorporates geometric properties to constrain the embedding space layout. Additionally, a spectral graph-theoretic method is developed to estimate the number of potential novel classes, offering high computational efficiency and flexibility. The proposed approach is tested across various biomedical scenarios, demonstrating its effectiveness and general applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning can help us discover new things in science by analyzing big datasets. However, when working with biomedical data, there are some challenges that make it hard to find new patterns or concepts. One problem is that the data isn’t evenly distributed, and another issue is that some groups of classes have a lot more data than others. This can lead to confusing and biased results. To solve this, researchers developed a new way to analyze the data using geometric constraints. This approach helps to minimize risks when learning about new concepts and structures. The team tested their method on different biomedical scenarios and found it worked well. |
Keywords
* Artificial intelligence * Embedding * Embedding space * Machine learning
