Medical-based Deep Curriculum Learning for Improved Fracture Classification
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This talk presents strategies derived from knowledge such as medical decision trees and inconsistencies in the annotations of multiple experts, which allow us to assign a degree of difficulty to each training sample. We demonstrate that if we start learning “easy” examples and move towards “hard”, the model can reach a better performance, even with fewer data.