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Alexander Cao

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AIIM Journal 2023 Journal Article

Open-set recognition of breast cancer treatments

Alexander Cao
Diego Klabjan
Yuan Luo

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, classifying patients by 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 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 (14% average F1 increase compared to recent methods), but we also reexamine open-set recognition in terms of deployability to a clinical setting.

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AAAI Conference 2021 Conference Paper

Open-Set Recognition with Gaussian Mixture Variational Autoencoders

Alexander Cao
Yuan Luo
Diego Klabjan

In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation’s ability to distinguish unknown classes. In contrast, we train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. With this, our Gaussian mixture variational autoencoder (GMVAE) achieves more accurate and robust open-set classification results, with an average F1 increase of 0. 26, through extensive experiments aided by analytical results.

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