mlsanityData to 14 Oct 2026

AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine

arXiv 2203.02384 ↗First posted 4 Mar 2022eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 4 Mar 2022.

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11 of 24 references.

References · 11

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  1. 2025Mixture of Experts (MoE): A Big Data Perspectivecited by 18