AutoMO-Mixer: An Automated Multi-objective Mixer Model for Balanced, Safe and Robust Prediction in Medicine

نویسندگان

چکیده

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 many fields. However, more reliable AI model is needed image guided therapy. To achieve this goal, developing a balanced, safe robust with unified framework desirable. In study, new termed as automated multi-objective Mixer (AutoMO-Mixer) was developed, which utilized recent developed multiple layer perceptron (MLP-Mixer) base. build balanced model, sensitivity specificity were considered objective functions simultaneously training stage. Meanwhile, evidential reasoning based on entropy to testing The experiment optical coherence tomography dataset demonstrated that AutoMO-Mixer can obtain safer, results compared MLP-Mixer other available models.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2022

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-21014-3_12