Publisher Correction: Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy (npj Digital Medicine, (2023), 6, 1, (64), 10.1038/s41746-023-00813-y)

Abstract

Correction to: npj Digital Medicine, published online 12 April 2023 In this article the legend for Figure legends for 1 to 4 were incorrectly matched. The figure legends should have appeared as shown below. The original article has been corrected. (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) A Thirteen features, including seven deep learning-based features and six quantitative features. B The framework of developing ENDOANGEL-ED. HIS Hue, Saturation, Intensity. The prediction of the six feature indexes and the diagnostic result were presented on the left. A The performance of the seven ML models on the internal image test set. Random forest (RF) showed the best performance. B Six indexes were determined by the RF model and the corresponding weights. RF random forest, GNB Gaussian Naive Bayes, KNN k-Nearest Neighbor, LR logistic regression, DT decision tree, SVM support vector machine, GBDT gradient boosting decision tree. A Internal videos. B External videos.