Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology

By Shoshana B. Ginsburg, George Lee, Sahirzeeshan Ali, Anant Madabhushi. 2016

33 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

Citations in the corpus

• Image analysis and machine learning in digital pathology: Challenges and opportunities (2016, unknown): “However there are efforts underway to be able to find ways to relate transformed feature spaces (such as might be obtained via deep learning) to handcrafted attributes (Ginsburg et al., 2016).”
• Explainable Machine Learning for Scientific Insights and Discoveries (2019, unknown): “Reference [65], for example, proposes FINE (feature importance in nonlinear embeddings) for the analysis of”