TY - JOUR
T1 - Open-set recognition of breast cancer treatments
AU - Cao, Alexander
AU - Klabjan, Diego
AU - Luo, Yuan
N1 - Funding Information:
The work of the first author is supported by the Predoctoral Training Program in Biomedical Data Driven Discovery (BD3) at Northwestern University (National Library of Medicine Grant 5T32LM012203 ). The work of the last author is supported in part by NIH Grant R01LM013337 .
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2023/1
Y1 - 2023/1
N2 - 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.
AB - 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.
KW - Autoencoder
KW - Breast cancer treatments
KW - Deep neural networks
KW - Open-set recognition
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U2 - 10.1016/j.artmed.2022.102451
DO - 10.1016/j.artmed.2022.102451
M3 - Article
C2 - 36628788
AN - SCOPUS:85142532778
SN - 0933-3657
VL - 135
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 102451
ER -