  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.
