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

—Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images. This proceduretypically involves extraction of hundreds of features, which may be used to predict disease presence, aggressiveness, or outcome, from digitized images of tissue slides. Due to the “ curse of dimensionality” , constructing a robust and interpretable classifier is very challenging when the dimensionality of the feature space is high. Dimensionality reduction (DR) is one approach for reducing the dimensionality of the feature space to facilitate classifier construction. When DR is performed, however, it can be challenging to quantify the contribution of each of the original features to the final classification or prediction result. In QH it is often important not only to create an accurate classifier of disease presence and aggressiveness, but also to identify the features that contribute most substantially to class separability. T...

• Type: Journal
• Year: 2016
• Where: TMI
• Authors: Shoshana B. Ginsburg, George Lee, Sahirzeeshan Ali, Anant Madabhushi
• Keywords: Knowledge Management, TMI 2016