Electrical Engineering and Systems Science > Image and Video Processing
arXiv:2203.02384 (eess)
[Submitted on 4 Mar 2022]
Title:AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine
View a PDF of the paper titled AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine, by Xi Chen and 6 other authors
View PDFAbstract: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.
| Subjects: | Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2203.02384 [eess.IV] |
| (orarXiv:2203.02384v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2203.02384 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine, by Xi Chen and 6 other authors
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