Variational deep embedding-based active learning for the diagnosis of pneumonia

Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set. It can conduct a much more significant amount of training and achieve higher recognition accuracy in specific fields than the human brain. In this paper, we proposed an active learning framework called variational deep embedding-based active learning (VaDEAL) as a human-centric computing method to improve the accuracy of diagnosing pneumonia. Because active learning (AL) realizes label-efficient learning by labeling the most valuable queries, we propose a new AL strategy that incorporates clustering to improve the sampling quality. Our framework consists of a VaDE module, a task learner, and a sampling calculator. First, the VaDE performs unsupervised reduction and clustering of dimension over the entire data set. The end-to-end task learner obtains the embedding representations of the VaDE-processed sample while training the target classifier of the model. The sampling calculator will calculate the representativeness of the samples by VaDE, the uncertainty of the samples through task learning, and ensure the overall diversity of the samples by calculating the similarity constraints between the current and previous samples. With our novel design, the combination of uncertainty, representativeness, and diversity scores allows us to select the most informative samples for labeling, thus improving overall performance. With extensive experiments and evaluations performed on a large dataset, we demonstrate that our proposed method is superior to the state-of-the-art methods and has the highest accuracy in the diagnosis of pneumonia.