An Adaptive and Interpretable Framework for Biomedical Image Analysis.

Biomedical image analysis has benefited tremendously from the advent of artificial intelligence. Machine learning and deep learning-based algorithms are increasingly utilized in real time to assist clinicians with making crucial decisions for patients. Explainability and interpretability of these algorithms are critical for doctors and patients to develop trust in the automated decision making-process. Furthermore, designing specialized solutions based on clinical applications of these algorithms is non-trivial, due to the heterogeneity of imaging data available. Therefore, we propose an adaptive and interpretable framework for biomedical image analysis with novel applications to transcranial magnetic resonance-guided focused ultrasound thalamotomy for the treatment of essential tremor. The algorithm automatically configures itself to analyze brain lesions based on heterogeneous magnetic resonance images and subsequently predicts short-term clinical outcomes utilizing random forest and SHAP values, while ensuring interpretability for this process.