Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies

• URL: http://arxiv.org/abs/2404.08853v1
• Date: Fri, 12 Apr 2024 23:49:37 GMT
• Title: Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies
• Authors: Bala McRae-Posani, Andrei Holodny, Hrithwik Shalu, Joseph N Stember,
• Abstract summary: Uncertainty quantification plays a vital role in facilitating the practical implementation of AI in radiology. We employed DNE to train a simple Convolutional Neural Network (CNN) with MRI images of the eyes for binary classification. We found that subjective features appreciated by human radiologists explained images for which uncertainty was high.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty quantification plays a vital role in facilitating the practical implementation of AI in radiology by addressing growing concerns around trustworthiness. Given the challenges associated with acquiring large, annotated datasets in this field, there is a need for methods that enable uncertainty quantification in small data AI approaches tailored to radiology images. In this study, we focused on uncertainty quantification within the context of the small data evolutionary strategies-based technique of deep neuroevolution (DNE). Specifically, we employed DNE to train a simple Convolutional Neural Network (CNN) with MRI images of the eyes for binary classification. The goal was to distinguish between normal eyes and those with metastatic tumors called choroidal metastases. The training set comprised 18 images with choroidal metastases and 18 without tumors, while the testing set contained a tumor-to-normal ratio of 15:15. We trained CNN model weights via DNE for approximately 40,000 episodes, ultimately reaching a convergence of 100% accuracy on the training set. We saved all models that achieved maximal training set accuracy. Then, by applying these models to the testing set, we established an ensemble method for uncertainty quantification.The saved set of models produced distributions for each testing set image between the two classes of normal and tumor-containing. The relative frequencies permitted uncertainty quantification of model predictions. Intriguingly, we found that subjective features appreciated by human radiologists explained images for which uncertainty was high, highlighting the significance of uncertainty quantification in AI-driven radiological analyses.

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