@inproceedings{c4c0d13b46fb4f889c47013422417a38,
title = "AutoMO-Mixer: An Automated Multi-objective Mixer Model for Balanced, Safe and Robust Prediction in Medicine",
abstract = "Accurately identifying patient{\textquoteright}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.",
keywords = "Balance, Image guided diagnosis and therapy, Reliable artificial intelligence, Robustness, Safe",
author = "Xi Chen and Jiahuan Lv and Dehua Feng and Xuanqin Mou and Ling Bai and Shu Zhang and Zhiguo Zhou",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer\_Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 18-09-2022",
year = "2022",
doi = "10.1007/978-3-031-21014-3\_12",
language = "英语",
isbn = "9783031210136",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "111--120",
editor = "Chunfeng Lian and Xiaohuan Cao and Islem Rekik and Xuanang Xu and Zhiming Cui",
booktitle = "Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings",
}