Data-driven progress analysis of radiologists' consistent and precise capabilities to diagnose breast lesions based on active learning

In the information era, the limited historical diagnostic data of radiologists are usually available. Under the conditions, how to help improve their diagnostic consistency and precision is an important issue. To address this issue, this study proposes an active learning-based data augmentation approach using unlabeled data, including data augmentation and best regression model hierarchies. In the data augmentation hierarchy, data-driven evidential reasoning (ER) models are constructed with an improved ER rule, in which criterion weights and reliabilities are learned from training data by giving two assumptions. These models rank unlabeled data in a descending order according to informativeness and representativeness. A data-adding process and a label-giving process are then designed to add the appropriate pieces of unlabeled data to clusters of original training data and give labels to the added data to form augmented training data, respectively. In the best regression model hierarchy, the augmented training data and the testing data are used to determine the optimal model by using eight machine learning algorithms, which aims to improve consistent diagnostic capabilities of radiologists. In consideration of consistent diagnostic capabilities of radiologists, recommendations are designed to help improve their precise diagnostic capabilities. With the historical examination reports of the 16 radiologists in the two tertiary hospitals located in Hefei, Anhui, China, experiments are conducted to reveal that their consistent diagnostic capabilities are improved by the data augmentation approach. Appropriate recommendations are also generated to help improve their precise diagnostic capabilities. By discussing the managerial insights of data augmentation approach and the designed recommendations, they are validated to be useful to help improve consistent and precise decision capabilities in management scenarios.