Keywords: Prostate, Machine Learning/Artificial Intelligence, Spatial Transformer Network, Transfer learningIn this study, we propose a method to predict clinically significant state cancer based on MRI points of interest (POI) and classification network with multi-mode and multi-scale. Instead of the traditional method of manual delineation region-of-interest (ROI) to assist prediction, our method utilizes multi-scale input combined with Spatial Transformer Network (STN) to automatically adjust the adjust the scale of interest. This work also explored the possibility of predicting the grade of prostate cancer in a small amount of data using the method of transfer learning. Experiments show that this method has high prediction performance.
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