DWT-CV: Dense weight transfer-based cross validation strategy for model selection in biomedical data analysis

Abstract

Model selection for deep learning algorithms is an extremely important step in the process of extracting knowledge from limited data, especially in biomedical data. The common approach is to adopt cross-validation techniques to randomly divide a small subset of the training set as the validation data for parameter tuning and model selection. However, this method may choose a sub-optimal model due to insufficient data utilization, and the process, such as k-fold cross-validation, is cumbersome and time-consuming. In this study, we propose a dense weight transfer-based cross validation (DWT-CV) strategy for biomedical data analysis and use this strategy to improve the generalization of deep learning algorithms with reduced training time using weight transfer learning. DWT-CV utilizes a dense weight aggregation and weight transfer mechanism to make the model more general and converge faster during the cross validation. The effectiveness of the proposed strategy is evaluated on multiple experiments with three different domains including biomedical image classification, drug–target affinity prediction, and medical image segmentation. Extensive experimental results demonstrate that our proposed DWT-CV strategy can make several deep learning benchmark methods perform better on multiple biomedical datasets, which implies that it may be an alternative to the traditional cross validation criterion for model selection.