2022/01/09 by Alexander Cao, Cao, Alexander, Diego Klabjan +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Molecular Biology Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2201.02923
openalex publication_date 2022/01/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.