Open-Set Recognition of Breast Cancer Treatments
This work addresses the problem of classifying novel cancer drug cocktails for healthcare applications, but it is incremental as it reframes an existing method to a new domain.
The paper tackled open-set recognition for breast cancer treatments by applying an existing Gaussian mixture variational autoencoder model to patient data, achieving a 24.5% average F1 increase compared to a recent method and examining deployability in clinical settings.
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.