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Artificial intelligence has achieved superhuman performance in a variety of tasks. Unfortunately, this is often done without interpretable methods. In medicine, it is not sufficient to have an algorithm with maximum accuracy. The methods must be evaluated by experts. Our motivating task is to detect features of the eye using unlabeled data. We detect features using a Bayesian changepoint model. Changepoint detection can perform object recognition when applied to two dimensional feature spaces such as images. We present a formula for detecting any shape with the changepoint model. We then extend it to multiple features in order to capture the color of an object. The work presented here has the ability to incorporate prior information, the ability to handle images of varying granularity, can provide confidence estimates on object features in an image. It will serve as the foundation for a classification method with interpretable results.
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