AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine
This work addresses the problem of improving reliability in medical image diagnosis for healthcare applications, though it appears incremental as it builds on existing MLP-Mixer methods.
The study tackled the need for reliable AI in medical image diagnosis by developing AutoMO-Mixer, a model that balances sensitivity and specificity and uses evidential reasoning for safety and robustness, achieving safer and more balanced results on an optical coherence tomography dataset compared to existing models.
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.