Open-Set Recognition of Breast Cancer Treatments

TL;DR

This paper applies open-set recognition to breast cancer treatment prediction, using a Gaussian mixture variational autoencoder to improve accuracy and robustness over previous healthcare methods.

Contribution

It introduces a novel application of a Gaussian mixture variational autoencoder to healthcare open-set recognition, achieving state-of-the-art results in breast cancer treatment classification.

Findings

24.5% average F1 score increase over previous methods

More accurate and robust classification results

Reevaluation of open-set recognition deployability in clinical settings

Abstract

Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered, predicting cancer treatments can naturally be formulated in terms of an open-set recognition problem. Drawbacks, due to modeling unknown samples during training, arise from straightforward implementations of prior work in healthcare open-set learning. Accordingly, we reframe the problem methodology and apply a recent existing Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results, with a 24.5% average F1 increase compared to a recent method, but we also reexamine open-set recognition in terms of deployability to a clinical setting.