- Keywords: Classification, Deep Learning, Medical imaging
- TL;DR: Multiclass extension of the GEV activation to improve unbalanced multiclass classification,
- Abstract: Unbalanced data poses a challenge when training machine learning algorithms; the algorithm often overfits on the dominant class and neglects the smaller classes. While methods such as oversampling aim to rebalance the data, this can lead to overfitting. When a certain class is underrepresented, either because it a rare disease or few images exist then methods are needed which can adequately account for this. The generalized extreme value (GEV) activation has recently been proposed as a solution to highly unbalanced data; however, the GEV activation is only available for binary classification. We extend this to the multiclass case with the multiclass GEV (mGEV) activation. We conduct experiments on X-ray images, with three classes, showing much-improved performance over the commonly used softmax activation. Code for the mGEV activation is available at [https://github.com/JTBridge/GEV].
- Paper Type: methodological development
- Source Latex: zip
- Primary Subject Area: Detection and Diagnosis
- Secondary Subject Area: Learning with Noisy Labels and Limited Data
- Paper Status: original work, not submitted yet
- Source Code Url: https://github.com/JTBridge/GEV
- Data Set Url: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T
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