乳腺癌治疗的开放式识别。
Open-set recognition of breast cancer treatments.
发表日期：2023 Jan
作者：
Alexander Cao, Diego Klabjan, Yuan Luo
来源：
ARTIFICIAL INTELLIGENCE IN MEDICINE
摘要：
开放式识别通过将测试样本分类为来自训练中已知类别之一或“未知”来推广分类任务。随着不断发现改进治疗的新型癌症药物组合，按照治疗方式对患者进行分类可以自然地表述为开放式识别问题。由于在训练过程中对未知样本进行建模，因此在医疗保健开放式学习的先前工作的直接实现中出现了缺陷。因此，我们重新构思了问题的方法，并将最近的高斯混合变分自编码器模型应用于乳腺癌患者数据中，该模型在图像数据集上获得了最新的结果。我们不仅获得了更准确和稳健的分类结果（相比于最新的方法，平均F1值提高了14％），而且还重新审视了开放式识别在临床设置中的可部署性。版权所有©2022 Elsevier B.V.。保留所有权利。
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.Copyright © 2022 Elsevier B.V. All rights reserved.