Keywords: Machine Learning/Artificial Intelligence, Data AnalysisWe compare modern deep learning (DL)-based parameter-estimation methods to their traditional maximum-likelihood estimation (MLE) counterparts by evaluating each approach’s performance in two clinical classification tasks. This is motivated by recent work demonstrating the inherent bias-variance trade-off that differentiates different DL-based approaches. Results show how these trade-offs manifest in the ‘real world’ of tissue classification, and how they compare to the performance achievable with conventional iterative MLE.
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