Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes

•Active Learning (AL) was evaluated in Pathology image analyses workflows.•Current stage-of-the-art methods have shown to delivery limited improvements in our target application.•We have proposed a novel AL method that is more effective by reducing the number of data items annotated to achieve a given AUC.•Our method has also been optimized to reduce the computation time required, which resulted in about 2.1× acceleration. Background and Objective:Deep learning methods have demonstrated remarkable performance in pathology image analysis, but they require a large amount of annotated training data from expert pathologists. The aim of this study is to minimize the data annotation need in these analyses. Methods:Active learning (AL) is an iterative approach to training deep learning models. It was used in our context with a Tumor Infiltrating Lymphocytes (TIL) classification task to minimize annotation. State-of-the-art AL methods were evaluated with the TIL application and we have proposed and evaluated a more efficient and effective AL acquisition method. The proposed method uses data grouping based on imaging features and model prediction uncertainty to select meaningful training samples (image patches). Results:An experimental evaluation with a collection of cancer tissue images shows that: (i) Our approach reduces the number of patches required to attain a given AUC as compared to other approaches, and (ii) our optimization (subpooling) leads to AL execution time improvement of about 2.12×. Conclusions: This strategy enabled TIL based deep learning analyses using smaller annotation demand. We expect this approach may be used to build other analyses in digital pathology with fewer training samples.