论文标题
汽车混合器：一种用于平衡，安全和强大预测医学预测的自动多目标搅拌机模型
AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine
论文作者
论文摘要
通过医学图像准确识别患者的状态在诊断和治疗中起着重要作用。人工智能（AI），尤其是深度学习，在许多领域取得了巨大的成功。但是，在图像引导诊断和治疗中需要更可靠的AI模型。为了实现这一目标，希望使用统一框架开发平衡，安全和健壮的模型。在这项研究中，开发了一种称为自动化多目标混合器（自动混合）模型的新统一模型，该模型利用了最近开发的多层感知器搅拌机（MLP-mixer）作为基础。为了构建平衡模型，在训练阶段同时将灵敏度和特异性视为同时的目标函数。同时，开发了一种基于熵的新的证据推理，以在测试阶段实现安全稳健的模型。光学相干断层扫描数据集的实验表明，与MLP-Mixer和其他可用模型相比，汽车混合器可以获得更安全，更平衡，更健壮的结果。
Accurately identifying patient's status through medical images plays an important role in diagnosis and treatment. Artificial intelligence (AI), especially the deep learning, has achieved great success in many fields. However, more reliable AI model is needed in image guided diagnosis and therapy. To achieve this goal, developing a balanced, safe and robust model with a unified framework is desirable. In this study, a new unified model termed as automated multi-objective Mixer (AutoMO-Mixer) model was developed, which utilized a recent developed multiple layer perceptron Mixer (MLP-Mixer) as base. To build a balanced model, sensitivity and specificity were considered as the objective functions simultaneously in training stage. Meanwhile, a new evidential reasoning based on entropy was developed to achieve a safe and robust model in testing stage. The experiment on an optical coherence tomography dataset demonstrated that AutoMO-Mixer can obtain safer, more balanced, and robust results compared with MLP-Mixer and other available models.
